Tag Archives: Video

Experimenting with Automatic Video Creation From a Web Page

At Google, we're actively exploring how people can use creativity tools powered by machine learning and computational methods when producing multimedia content, from creating music and reframing videos, to drawing and more. One creative process in particular, video production, can especially benefit from such tools, as it requires a series of decisions about what content is best suited to a target audience, how to position the available assets within the field of view, and what temporal arrangement will yield the most compelling narrative. But what if one could leverage existing assets, such as a website, to get a jump-start on video creation? Businesses commonly host websites that contain rich visual representations about their services or products, all of which could be repurposed for other multimedia formats, such as videos, potentially enabling those without extensive resources the ability to reach a broader audience.

In “Automatic Video Creation From a Web Page”, published at UIST 2020, we introduce URL2Video, a research prototype pipeline to automatically convert a web page into a short video, given temporal and visual constraints provided by the content owner. URL2Video extracts assets (text, images, or videos) and their design styles (including fonts, colors, graphical layouts, and hierarchy) from HTML sources and organizes the visual assets into a sequence of shots, while maintaining a look-and-feel similar to the source page. Given a user-specified aspect ratio and duration, it then renders the repurposed materials into a video that is ideal for product and service advertising.

URL2Video Overview
Assume a user provides an URL to a web page that illustrates their business. The URL2Video pipeline automatically selects key content from the page and decides the temporal and visual presentation of each asset, based on a set of heuristics derived from an interview study with designers who were familiar with web design and video ad creation. These designer-informed heuristics capture common video editing styles, including content hierarchy, constraining the amount of information in a shot and its time duration, providing consistent color and style for branding, and more. Using this information, the URL2Video pipeline parses a web page, analyzing the content and selecting visually salient text or images while preserving their design styles, which it organizes according to the video specifications provided by the user.

By extracting the structural content and design from the input web page, URL2Video makes automatic editing decisions to present key messages in a video. It considers the temporal (e.g., the duration in seconds) and spatial (e.g., the aspect ratio) constraints of the output video defined by users.

Webpage Analysis
Given a webpage URL, URL2Video extracts document object model (DOM) information and multimedia materials. For the purposes of our research prototype, we limited the domain to static web pages that contain salient assets and headings preserved in an HTML hierarchy that follows recent web design principles, which encourage the use of prominent elements, distinct sections, and an order of visual focus that guides readers in perceiving information. URL2Video identifies such visually-distinguishable elements as a candidate list of asset groups, each of which may contain a heading, a product image, detailed descriptions, and call-to-action buttons, and captures both the raw assets (text and multimedia files) and detailed design specifications (HTML tags, CSS styles, and rendered locations) for each element. It then ranks the asset groups by assigning each a priority score based on their visual appearance and annotations, including their HTML tags, rendered sizes, and ordering shown on the page. In this way, an asset group that occupies a larger area at the top of the page receives a higher score.

Constraints-Based Asset Selection
We consider two goals when composing a video: (1) each video shot should provide concise information, and (2) the visual design should be consistent with the source page. Based on these goals and the video constraints provided by the user, including the intended video duration (in seconds) and aspect ratio (commonly 16:9, 4:3, 1:1, etc.), URL2Video automatically selects and orders the asset groups to optimize the total priority score. To make the content concise, it presents only dominant elements from a page, such as a headline and a few multimedia assets. It constrains the duration of each visual element for viewers to perceive the content. In this way, a short video highlights the most salient information from the top of the page, and a longer video contains more campaigns or products.

Scene Composition & Video Rendering
Given an ordered list of assets based on the DOM hierarchy, URL2Video follows the design heuristics obtained from interview studies to make decisions about both the temporal and spatial arrangement to present the assets in individual shots. It transfers the graphical layout of elements into the video’s aspect ratio, and applies the style choices including fonts and colors. To make a video more dynamic and engaging, it adjusts the presentation timing of assets. Finally, it renders the content into a video in the MPEG-4 container format.

User Control
The interface to the research prototype allows the user to review the design attributes in each video shot extracted from the source page, reorder the materials, change the detailed design, such as colors and fonts, and adjust the constraints to generate a new video.

In URL2Video's authoring interface (left), users specify the input URL to a source page, size of the target page view, and the output video parameters. URL2Video analyzes the web page and extracts major visual components. It composes a series of scenes and visualizes the key frames as a storyboard. These components are rendered into an output video that satisfies the input temporal and spatial constraints. Users can playback the video, examine the design attributes (bottom-right), and make adjustments to generate video variation, such as reordering the scenes (top-right).

URL2Video Use Cases
We demonstrate the performance of the end-to-end URL2Video pipeline on a variety of existing web pages. Below we highlight an example result where URL2Video converts a page that embeds multiple short video clips into a 12-second output video. Note how the pipeline makes automatic editing decisions on font and color choices, timing, and content ordering in a video captured from the source page.

URL2Video identifies key content from our Google Search introduction page (top), including headings and video assets. It converts them into a video by considering the presentation flow, the source design and the output constraints (a 12-second landscape video; bottom).

The video below provides further demonstration:

To evaluate the automatically-generated videos, we conducted a user study with designers at Google. Our results show that URL2Video effectively extracted design elements from a web page and supported designers by bootstrapping the video creation process.

Next steps
While this current research focuses on the visual presentation, we are developing new techniques that support the audio track and a voiceover in video editing. All in all, we envision a future where creators focus on making high-level decisions and an ML model interactively suggests detailed temporal and graphical edits for a final video creation on multiple platforms.

We greatly thank our paper co-authors, Zheng Sun (Research) and Katrina Panovich (YouTube). We would also like to thank our colleagues who contributed to URL2Video, (in alphabetical order of last name) Jordan Canedy, Brian Curless, Nathan Frey, Madison Le, Alireza Mahdian, Justin Parra, Emily Ryan, Mogan Shieh, Sandor Szego, and Weilong Yang. We are grateful to receive the support from our leadership, Tomas Izo, Rahul Sukthankar, and Jay Yagnik.

Source: Google AI Blog

RepNet: Counting Repetitions in Videos

Repeating processes ranging from natural cycles, such as phases of the moon or heartbeats and breathing, to artificial repetitive processes, like those found on manufacturing lines or in traffic patterns, are commonplace in our daily lives. Beyond just their prevalence, repeating processes are of interest to researchers for the variety of insights one can tease out of them. It may be that there is an underlying cause behind something that happens multiple times, or there may be gradual changes in a scene that may be useful for understanding. Sometimes, repeating processes provide us with unambiguous “action units”, semantically meaningful segments that make up an action. For example, if a person is chopping an onion, the action unit is the manipulation action that is repeated to produce additional slices. These units may be indicative of more complex activity and may allow us to analyze more such actions automatically at a finer time-scale without having a person annotate these units. For the above reasons, perceptual systems that aim to observe and understand our world for an extended period of time will benefit from a system that understands general repetitions.

In “Counting Out Time: Class Agnostic Video Repetition Counting in the Wild”, we present RepNet, a single model that can understand a broad range of repeating processes, ranging from people exercising or using tools, to animals running and birds flapping their wings, pendulums swinging, and a wide variety of others. In contrast to our previous work, which used cycle-consistency constraints across different videos of the same action to understand them at a fine-grained level, in this work we present a system that can recognize repetitions within a single video. Along with this model, we are releasing a dataset to benchmark class-agnostic counting in videos and a Colab notebook to run RepNet.

RepNet is a model that takes as input a video that contains periodic action of a variety of classes (including those unseen during training) and returns the period of repetitions found therein. In the past the problem of repetition counting has been addressed by directly comparing pixel intensities in frames, but real world videos have camera motion, occlusion by objects in the field, drastic scale difference and changes in form, which necessitates learning of features invariant to such noise. To accomplish this we train a machine learning model in an end-to-end manner to directly estimate the period of the repetitions. The model consists of three parts: a frame encoder, an intermediate representation, called a temporal self-similarity matrix (which we will describe below), and a period predictor.

First, the frame encoder uses the ResNet architecture as a per-frame model to generate embeddings of each frame of the video The ResNet architecture was chosen since it has been successful for a number of image and video tasks. Passing each frame of a video through a ResNet-based encoder yields a sequence of embeddings.

At this point we calculate a temporal self-similarity matrix (TSM) by comparing each frame’s embedding with every other frame in the video, returning a matrix that is easy for subsequent modules to analyze for counting repetitions. This process surfaces self-similarities in the stream of video frames that enable period estimation, as demonstrated in the video below.
Demonstration of how the TSM processes images of the Earth’s day-night cycle.
For each frame, we then use Transformers to predict the period of repetition and the periodicity (i.e., whether or not a frame is part of the periodic process) directly from the sequence of similarities in the TSM. Once we have the period, we obtain the per-frame count by dividing the number of frames captured in a periodic segment by the period length. We sum this up to predict the number of repetitions in the video.
Overview of the RepNet model.
Temporal Self-Similarity Matrix
The example of the TSM from the day-night cycle, shown above, is derived from an idealized scenario with fixed period repetitions. TSMs from real videos often reveal fascinating structures in the world, as demonstrated in the three examples below. Jumping jacks are close to the ideal periodic action with a fixed period, while in contrast, the period of a bouncing ball declines as the ball loses energy through repeated bounces. The video of someone mixing concrete demonstrates repetitive action that is preceded and followed by a period without motion. These three behaviors are clearly distinguished in the learned TSM, which requires that the model pay attention to fine changes in the scene.
Jumping Jacks (constant period; video from Kinetics), Bouncing ball (decreasing period; Kinetics), Mixing concrete (aperiodic segments present in video; PERTUBE dataset).
One advantage of using the TSM as an intermediate layer in RepNet is that the subsequent processing by the transformers is done in the self-similarity space and not in the feature space. This encourages generalization to unseen classes. For example, the TSMs produced by actions as different as jumping jacks or swimming are similar as long as the action was repeated at a similar pace. This allows us to train on some classes and yet expect generalization to unseen classes.

One way to train the above model would be to collect a large dataset of videos that capture repetitive activities and label them with the repetition count. The challenge in this is two-fold. First, it requires one to examine a large number of videos to identify those with repeated actions. Following that, each video must be annotated with the number of times an action was repeated. While for certain tasks annotators can skip frames (for example, to classify a video as showing jumping jacks), they still need to see the entire video in order to count how many jumping jacks were performed.

We overcome this challenge by introducing a process for synthetic data generation that produces videos with repetitions using videos that may not contain repeating actions at all. This is accomplished by randomly selecting a segment of the video to repeat an arbitrary number of times, bookended by the original video context.
Our synthetic data generation pipeline that produces videos with repetitions from any video.
While this process generates a video that resembles a natural-looking video with repeating processes, it is still too simple for deep learning methods, which can learn to cheat by looking for artifacts, instead of learning to recognize repetitions. To address this, we perform extreme data augmentation, which we call camera motion augmentation. In this method, we modify the video to simulate a camera that smoothly moves around using 2D affine motion as the video progresses.
Left: An example of a synthetic repeating video generated from a random video. Right: An example of a video with camera motion augmentation, which is tougher for the model, but results in better generalization to real repeating videos (both from Kinetics).
Even though we can train a model on synthetic repeating videos, the resulting models must be able to generalize to real video of repeating processes. In order to evaluate the performance of the trained models on real videos, we collect a dataset of ~9000 videos from the Kinetics dataset. These videos span many action classes and capture diverse scenes, arising from the diversity of data seen on Youtube. We annotate these videos with the count of the action being repeated in the video. To encourage further research in this field, we are releasing the count annotations for this dataset, which we call Countix.

A class-agnostic counting model has many useful applications. RepNet serves as a single model that can count repetitions from many different domains:
RepNet can count repeated activities from a range of domains, such as slicing onions (left; video from Kinetics dataset), Earth’s diurnal cycle (middle; Himawari satellite data), or even a cheetah in motion (right; video from imgur.com).
RepNet could be used to estimate heartbeat rates from echocardiogram videos even though it has not seen such videos in training:
Predicted heart rates: 45 bpm (left) and 75 bpm (right). True heart rates 46-50 bpm and 78-79 bpm, respectively. RepNet’s prediction of the heart rate across different devices is encouragingly close to the rate measured by the device. (Source for left and right)
RepNet can also be used to monitor repeating activities for any changes in speed. Below we show how the Such changes in speed can also be used in other settings for quality or process control.
In this video, we see RepNet counting accelerating cellular oscillations observed under a laser microscope even though it has never seen such a video during training, (from Nature article).
Left: Person performing a “mountain climber” exercise. Right: The 1D projection of the RepNet embeddings using principal component analysis, capturing the moment that the person changes their speed during the exercise. (Video from Kinetics)
We are releasing Countix annotations for the community to work on the problem of repetition counting. We are also releasing a Colab notebook for running RepNet. Using this you can run RepNet on your videos or even using your webcam to detect periodic activities in video and count repetitions automatically in videos.

This is joint work with Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. Special thanks to Tom Small for designing the visual explanation of TSM. The authors thank Anelia Angelova, Relja Arandjelović, Sourish Chaudhuri, Aishwarya Gomatam, Meghana Thotakuri, and Vincent Vanhoucke for their help with this project.

Source: Google AI Blog

Celebrating 10 years of WebM and WebRTC

Originally posted on the Chromium Blog

Ten years ago, Google planted the seeds for two foundational web media technologies, hoping they would provide the roots for a more vibrant internet. Two acquisitions, On2 Technologies and Global IP Solutions, led to a pair of open source projects: the WebM Project, a family of cutting edge video compression technologies (codecs) offered by Google royalty-free, and the WebRTC Project building APIs for real-time voice and video communication on the web.

These initiatives were major technical endeavors, essential infrastructure for enabling the promise of HTML5 with support for video conferencing and streaming. But this was also a philosophical evolution for media as Product Manager Mike Jazayeri noted in his blog post hailing the launch of the WebM Project:
“A key factor in the web’s success is that its core technologies such as HTML, HTTP, TCP/IP, etc. are open and freely implementable.”
As emerging first-class participants in the web experience, media and communication components also had to be free and open.

A decade later, these principles have ensured compression and communication technologies capable of keeping pace with a web ecosystem characterized by exponential growth of media consumption, devices, and demand. Starting from VP8 in 2010, the WebM Project has delivered up to 50% video bitrate savings with VP9 in 2013 and an additional 30% with AV1 in 2018—with adoption by YouTube, Facebook, Netflix, Twitch, and more. Equally importantly, the WebM team co-founded the Alliance for Open Media which has brought the IP of over 40 major tech companies in support of open and free codecs. With Chrome, Edge, Firefox and Safari supporting WebRTC, more than 85% of all installed browsers globally have become a client for real-time communications on the Internet. WebRTC has become a stable standard and it is now the default solution for video calling on the Web. These technologies have succeeded together, as today over 90% of encoded WebRTC video in Chrome uses VP8 or VP9.

The need for these technologies has been highlighted by COVID-19, as people across the globe have found new ways to work, educate, and connect with loved ones via video chat. The compression of open codecs has been essential to keeping services running on limited bandwidth, with over a billion hours of VP9 and AV1 content viewed every day. WebRTC has allowed for an ecosystem of interoperable communications apps to flourish: since the beginning of March 2020, we have seen in Chrome a 13X increase in received video streams via WebRTC.

These successes would not have been possible without all the supporters that make an open source community. Thank you to all the code contributors, testers, bug filers, and corporate partners who helped make this ecosystem a reality. A decade in, Google remains as committed as ever to open media on the web. We look forward to continuing that work with all of you in the next decade and beyond.

By Matt Frost, Product Director Chrome Media and Niklas Blum, Senior Product Manager WebRTC

AutoFlip: An Open Source Framework for Intelligent Video Reframing

Originally posted on the AI Blog

Videos filmed and edited for television and desktop are typically created and viewed in landscape aspect ratios (16:9 or 4:3). However, with an increasing number of users creating and consuming content on mobile devices, historical aspect ratios don’t always fit the display being used for viewing. Traditional approaches for reframing video to different aspect ratios usually involve static cropping, i.e., specifying a camera viewport, then cropping visual contents that are outside. Unfortunately, these static cropping approaches often lead to unsatisfactory results due to the variety of composition and camera motion styles. More bespoke approaches, however, typically require video curators to manually identify salient contents on each frame, track their transitions from frame-to-frame, and adjust crop regions accordingly throughout the video. This process is often tedious, time-consuming, and error-prone.

To address this problem, we are happy to announce AutoFlip, an open source framework for intelligent video reframing. AutoFlip is built on top of the MediaPipe framework that enables the development of pipelines for processing time-series multimodal data. Taking a video (casually shot or professionally edited) and a target dimension (landscape, square, portrait, etc.) as inputs, AutoFlip analyzes the video content, develops optimal tracking and cropping strategies, and produces an output video with the same duration in the desired aspect ratio.
Left: Original video (16:9). Middle: Reframed using a standard central crop (9:16). Right: Reframed with AutoFlip (9:16). By detecting the subjects of interest, AutoFlip is able to avoid cropping off important visual content.

AutoFlip Overview

AutoFlip provides a fully automatic solution to smart video reframing, making use of state-of-the-art ML-enabled object detection and tracking technologies to intelligently understand video content. AutoFlip detects changes in the composition that signify scene changes in order to isolate scenes for processing. Within each shot, video analysis is used to identify salient content before the scene is reframed by selecting a camera mode and path optimized for the contents.

Shot (Scene) Detection

A scene or shot is a continuous sequence of video without cuts (or jumps). To detect the occurrence of a shot change, AutoFlip computes the color histogram of each frame and compares this with prior frames. If the distribution of frame colors changes at a different rate than a sliding historical window, a shot change is signaled. AutoFlip buffers the video until the scene is complete before making reframing decisions, in order to optimize the reframing for the entire scene.

Video Content Analysis

We utilize deep learning-based object detection models to find interesting, salient content in the frame. This content typically includes people and animals, but other elements may be identified, depending on the application, including text overlays and logos for commercials, or motion and ball detection for sports.

The face and object detection models are integrated into AutoFlip through MediaPipe, which uses TensorFlow Lite on CPU. This structure allows AutoFlip to be extensible, so developers may conveniently add new detection algorithms for different use cases and video content. Each object type is associated with a weight value, which defines its relative importance — the higher the weight, the more influence the feature will have when computing the camera path.

Left: People detection on sports footage. Right: Two face boxes (‘core’ and ‘all’ face landmarks). In narrow portrait crop cases, often only the core landmark box can fit.


After identifying the subjects of interest on each frame, logical decisions about how to reframe the content for a new view can be made. AutoFlip automatically chooses an optimal reframing strategy — stationary, panning or tracking — depending on the way objects behave during the scene (e.g., moving around or stationary). In stationary mode, the reframed camera viewport is fixed in a position where important content can be viewed throughout the majority of the scene. This mode can effectively mimic professional cinematography in which a camera is mounted on a stationary tripod or where post-processing stabilization is applied. In other cases, it is best to pan the camera, moving the viewport at a constant velocity. The tracking mode provides continuous and steady tracking of interesting objects as they move around within the frame.

Based on which of these three reframing strategies the algorithm selects, AutoFlip then determines an optimal cropping window for each frame, while best preserving the content of interest. While the bounding boxes track the objects of focus in the scene, they typically exhibit considerable jitter from frame-to-frame and, consequently, are not sufficient to define the cropping window. Instead, we adjust the viewport on each frame through the process of Euclidean-norm optimization, in which we minimize the residuals between a smooth (low-degree polynomial) camera path and the bounding boxes.

Top: Camera paths resulting from following the bounding boxes from frame-to-frame. Bottom: Final smoothed camera paths generated using Euclidean-norm path formation. Left: Scene in which objects are moving around, requiring a tracking camera path. Right: Scene where objects stay close to the same position; a stationary camera covers the content for the full duration of the scene.

AutoFlip’s configuration graph provides settings for either best-effort or required reframing. If it becomes infeasible to cover all the required regions (for example, when they are too spread out on the frame), the pipeline will automatically switch to a less aggressive strategy by applying a letterbox effect, padding the image to fill the frame. For cases where the background is detected as being a solid color, this color is used to create seamless padding; otherwise a blurred version of the original frame is used.

AutoFlip Use Cases

We are excited to release this tool directly to developers and filmmakers, reducing the barriers to their design creativity and reach through the automation of video editing. The ability to adapt any video format to various aspect ratios is becoming increasingly important as the diversity of devices for video content consumption continues to rapidly increase. Whether your use case is portrait to landscape, landscape to portrait, or even small adjustments like 4:3 to 16:9, AutoFlip provides a solution for intelligent, automated and adaptive video reframing.

What’s Next?

Like any machine learning algorithm, AutoFlip can benefit from an improved ability to detect objects relevant to the intent of the video, such as speaker detection for interviews or animated face detection on cartoons. Additionally, a common issue arises when input video has important overlays on the edges of the screen (such as text or logos) as they will often be cropped from the view. By combining text/logo detection and image inpainting technology, we hope that future versions of AutoFlip can reposition foreground objects to better fit the new aspect ratios. Lastly, in situations where padding is required, deep uncrop technology could provide improved ability to expand beyond the original viewable area.

While we work to improve AutoFlip internally at Google, we encourage contributions from developers and filmmakers in the open source communities.


We would like to thank our colleagues who contributed to Autoflip, Alexander Panagopoulos, Jenny Jin, Brian Mulford, Yuan Zhang, Alex Chen, Xue Yang, Mickey Wang, Justin Parra, Hartwig Adam, Jingbin Wang, and Weilong Yang; MediaPipe team who helped with open sourcing, Jiuqiang Tang, Tyler Mullen, Mogan Shieh, Ming Guang Yong, and Chuo-Ling Chang.

By Nathan Frey, Senior Software Engineer, Google Research, Los Angeles and Zheng Sun, Senior Software Engineer, Google Research, Mountain View

Video Series for New Webmasters: Search for Beginners!

We are excited to introduce our newest video series: “Search For Beginners”! The series was created primarily to help new webmasters. It is also for anyone with an interest in Search or anyone who is still learning about the Web and how to manage their online presence.

We love to see the webmaster community grow! Every day, there are countless new webmasters who are taking the first steps in learning how Search works, and how to make their websites perform well and discoverable on Search. We understand that it sometimes can be challenging or even overwhelming to start with our existing content without some prior knowledge or basic understandings of the Web. We find our basic videos in our YouTube channels to be the ones with the most views. At the same time, advanced webmasters also see the need for content that can be sent to clients or stakeholders to help explain important concepts in managing an online presence.

We want to help all webmasters succeed, regardless of whether you have been managing websites for many years or you’ve just started out yesterday. We want to do more to help the new webmasters and this video series will hopefully help us achieve that.

Introduction to the series:

Episode 1:

The “Search For Beginners” video series covers basic online presence topics ranging from ‘Do you need a website?’, ‘What are the goals for your website?’ to more organic search-related topics such as ‘How does Google Search work?’, ‘How to change description line’, or ‘How to change wrong address information on Google’. Actually, we get asked these questions frequently in forums, social channels and at events around the world! The videos are fully animated. The videos are in English with subtitles available in Spanish, Portuguese, Korean, Chinese, Indonesian, Italian, Japanese, and English. We are working on more, so please stay tuned!

And if you consider yourself a more experienced user, please feel free to use these videos to support your pitches or explaining things to your clients. If you want to share any ideas or learnings, please leave them in the comment section in each video so that others can benefit from your knowledge and experience.

Follow us on Twitter and subscribe on YouTube for the upcoming videos! We will be adding new videos in this series to this playlist about every two weeks!

Video Series for New Webmasters: Search for Beginners!

We are excited to introduce our newest video series: “Search For Beginners”! The series was created primarily to help new webmasters. It is also for anyone with an interest in Search or anyone who is still learning about the Web and how to manage their online presence.

We love to see the webmaster community grow! Every day, there are countless new webmasters who are taking the first steps in learning how Search works, and how to make their websites perform well and discoverable on Search. We understand that it sometimes can be challenging or even overwhelming to start with our existing content without some prior knowledge or basic understandings of the Web. We find our basic videos in our YouTube channels to be the ones with the most views. At the same time, advanced webmasters also see the need for content that can be sent to clients or stakeholders to help explain important concepts in managing an online presence.

We want to help all webmasters succeed, regardless of whether you have been managing websites for many years or you’ve just started out yesterday. We want to do more to help the new webmasters and this video series will hopefully help us achieve that.

Introduction to the series:

Episode 1:

The “Search For Beginners” video series covers basic online presence topics ranging from ‘Do you need a website?’, ‘What are the goals for your website?’ to more organic search-related topics such as ‘How does Google Search work?’, ‘How to change description line’, or ‘How to change wrong address information on Google’. Actually, we get asked these questions frequently in forums, social channels and at events around the world! The videos are fully animated. The videos are in English with subtitles available in Spanish, Portuguese, Korean, Chinese, Indonesian, Italian, Japanese, and English. We are working on more, so please stay tuned!

And if you consider yourself a more experienced user, please feel free to use these videos to support your pitches or explaining things to your clients. If you want to share any ideas or learnings, please leave them in the comment section in each video so that others can benefit from your knowledge and experience.

Follow us on Twitter and subscribe on YouTube for the upcoming videos! We will be adding new videos in this series to this playlist about every two weeks!

Audio and Visual Quality Measurement using Fréchet Distance

"I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.”
    William Thomson (Lord Kelvin), Lecture on "Electrical Units of Measurement" (3 May 1883), published in Popular Lectures Vol. I, p. 73
The rate of scientific progress in machine learning has often been determined by the availability of good datasets, and metrics. In deep learning, benchmark datasets such as ImageNet or Penn Treebank were among the driving forces that established deep artificial neural networks for image recognition and language modeling. Yet, while the available “ground-truth” datasets lend themselves nicely as measures of performance on these prediction tasks, determining the “ground-truth” for comparison to generative models is not so straightforward. Imagine a model that generates videos of StarCraft video game sequences — how does one determine which model is best? Clearly some of the videos shown below look more realistic than others, but can the differences between them be quantified? Access to robust metrics for evaluation of generative models is crucial for measuring (and making) progress in the fields of audio and video understanding, but currently no such metrics exist.
Videos generated from various models trained on sequences from the StarCraft Video (SCV) dataset.
In “Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms” and “Towards Accurate Generative Models of Video: A New Metric & Challenges”, we present two such metrics — the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). We document our large-scale human evaluations using 10k video and 69k audio clip pairwise comparisons that demonstrate high correlations between our metrics and human perception. We are also releasing the source code for both Fréchet Video Distance and Fréchet Audio Distance on github (FVD; FAD).

General Description of Fréchet Distance
The goal of a generative model is to learn to produce samples that look similar to the ones on which it has been trained, such that it knows what properties and features are likely to appear in the data, and which ones are unlikely. In other words, a generative model must learn the probability distribution of the training data. In many cases, the target distributions for generative models are very high-dimensional. For example, a single image of 128x128 pixels with 3 color channels has almost 50k dimensions, while a second-long video clip might consist of dozens (or hundreds) of such frames with audio that may have 16,000 samples. Calculating distances between such high dimensional distributions in order to quantify how well a given model succeeds at a task is very difficult. In the case of pictures, one could look at a few samples to gauge visual quality, but doing so for every model trained is not feasible.

In addition, generative adversarial networks (GANs) tend to focus on a few modes of the overall target distribution, while completely ignoring others. For example, they may learn to generate only one type of object or only a select few viewing angles. As a consequence, looking only at a limited number of samples from the model may not indicate whether the network learned the entire distribution successfully. To remedy this, a metric is needed that aligns well with human judgement of quality, while also taking the properties of the target distribution into account.

One common solution for this problem is the so-called Fréchet Inception Distance (FID) metric, which was specifically designed for images. The FID takes a large number of images from both the target distribution and the generative model, and uses the Inception object-recognition network to embed each image into a lower-dimensional space that captures the important features. Then it computes the so-called Fréchet distance between these samples, which is a common way of calculating distances between distributions that provides a quantitative measure of how similar the two distributions actually are.
A key component for both metrics is a pre-trained model that converts the video or audio clip into an N-dimensional embedding.
Fréchet Audio Distance and Fréchet Video Distance
Building on the principles of FID that have been successfully applied to the image domain, we propose both Fréchet Video Distance (FVD) and Fréchet Audio Distance (FAD). Unlike popular metrics such as peak signal-to-noise ratio or the structural similarity index, FVD looks at videos in their entirety, and thereby avoids the drawbacks of framewise metrics.
Examples of videos of a robot arm, judged by the new FVD metric. FVD values were found to be approximately 2000, 1000, 600, 400, 300 and 150 (left-to-right; top-to-bottom). A lower FVD clearly correlates with higher video quality.
In the audio domain, existing metrics either require a time-aligned ground truth signal, such as source-to-distortion ratio (SDR), or only target a specific domain, like speech quality. FAD on the other hand is reference-free and can be used on any type of audio.

Below is a 2-D visualization of the audio embedding vectors from which we compute the FAD. Each point corresponds to the embedding of a 5-second audio clip, where the blue points are from clean music and other points represent audio that has been distorted in some way. The estimated multivariate Gaussian distributions are presented as concentric ellipses. As the magnitude of the distortions increase, the overlap between their distributions and that of the clean audio decreases. The separation between these distributions is what the Fréchet distance is measuring.
In the animation, we can see that as the magnitude of the distortions increases, the Gaussian distributions of the distorted audio overlaps less with the clean distribution. The magnitude of this separation is what the Fréchet distance is measuring.
It is important for FAD and FVD to closely track human judgement, since that is the gold standard for what looks and sounds “realistic”. So, we performed a large-scale human study to determine how well our new metrics align with qualitative human judgment of generated audio and video. For the study, human raters examined 10,000 video pairs and 69,000 5-second audio clips. For the FAD we asked human raters to compare the effect of two different distortions on the same audio segment, randomizing both the pair of distortions that they compared and the order in which they appeared. The raters were asked “Which audio clip sounds most like a studio-produced recording?” The collected set of pairwise evaluations was then ranked using a Plackett-Luce model, which estimates a worth value for each parameter configuration. Comparison of the worth values to the FAD demonstrates that the FAD correlates quite well with human judgement.
This figure compares the FAD calculated between clean background music and music distorted by a variety of methods (e.g., pitch down, Gaussian noise, etc.) to the associated worth values from human evaluation. Each type of distortion has two data points representing high and low extremes in the distortion applied. The quantization distortion (purple circles), for example, limits the audio to a specific number of bits per sample, where the two data points represent two different bitrates. Both human raters and the FAD assigned higher values (i.e., “less realistic”) to the lower bitrate quantization. Overall log FAD correlates well with human judgement — a perfect correlation between the log FAD and human perception would result in a straight line.
We are currently making great strides in generative models. FAD and FVD will help us keeping this progress measurable, and will hopefully lead us to improve our models for audio and video generation.

There are many people who contributed to this large effort, and we’d like to highlight some of the key contributors: Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, Sylvain Gelly, Mauricio Zuluaga, Dominik Roblek, Matthew Sharifi as well as the extended Google Brain team in Zurich.

Source: Google AI Blog

Video Understanding Using Temporal Cycle-Consistency Learning

In the last few years there has been great progress in the field of video understanding. For example, supervised learning and powerful deep learning models can be used to classify a number of possible actions in videos, summarizing the entire clip with a single label. However, there exist many scenarios in which we need more than just one label for the entire clip. For example, if a robot is pouring water into a cup, simply recognizing the action of “pouring a liquid” is insufficient to predict when the water will overflow. For that, it is necessary to track frame-by-frame the amount of water in the cup as it is being filled. Similarly, a baseball coach who is comparing stances of pitchers may want to retrieve video frames from the precise moment that the ball leaves the pitchers’ hands. Such applications require models to understand each frame of a video.

However, applying supervised learning to understand each individual frame in a video is expensive, since per-frame labels in videos of the action of interest are needed. This requires that annotators apply fine-grained labels to videos by manually adding unambiguous labels to every frame in each video. Only then can the model be trained, and only on a single action. Training on new actions requires the process to be repeated. With the increasing demand for fine-grained labeling, necessary for applications ranging from robotics to sports analytics, this makes the need for scalable learning algorithms that can understand videos without the tedious labeling process increasingly pertinent.

We propose a potential solution using a self-supervised learning method called Temporal Cycle-Consistency Learning (TCC). This novel approach uses correspondences between examples of similar sequential processes to learn representations particularly well-suited for fine-grained temporal understanding of videos. We are also releasing our TCC codebase to enable end-users to apply our self-supervised learning algorithm to new and novel applications.

Representation Learning Using TCC
A plant growing from a seedling to a tree; the daily routine of getting up, going to work and coming back home; or a person pouring themselves a glass of water are all examples of events that happen in a particular order. Videos capturing such processes provide temporal correspondences across multiple instances of the same process. For example, when pouring a drink one could be reaching for a teapot, a bottle of wine, or a glass of water to pour from. Key moments are common to all pouring videos (e.g., the first touch to the container or the container being lifted from the ground) and exist independent of many varying factors, such as visual changes in viewpoint, scale, container style, or the speed of the event. TCC attempts to find such correspondences across videos of the same action by leveraging the principle of cycle-consistency, which has been applied successfully in many problems in computer vision, to learn useful visual representations by aligning videos.

The objective of this training algorithm is to learn a frame encoder, using any network architecture that processes images, such as ResNet. To do so, we pass all frames of the videos to be aligned through the encoder to produce their corresponding embeddings. We then select two videos for TCC learning, say video 1 (the reference video) and video 2. A reference frame is chosen from video 1 and its nearest neighbor frame (NN2) from video 2 is found in the embedding space (not pixel space). We then cycle back by finding the nearest neighbor of NN2 in video 1, which we call NN1. If the representations are cycle-consistent, then the nearest neighbor frame in video 1 (NN1) should refer back to the starting reference frame.
We train the embedder using the distance between the starting reference frame and NN1 as the training signal. As training proceeds, the embeddings improve and reduce the cycle-consistency loss by developing a semantic understanding of each video frame in the context of the action being performed.
Using TCC, we learn embeddings with temporally fine-grained understanding of an action by aligning related videos.
What Does TCC Learn?
In the following figure, we show a model trained using TCC on videos from the Penn Action Dataset of people performing squat exercises. Each point on the left corresponds to frame embeddings, with the highlighted points tracking the embedding of the current video frame. Notice how the embeddings move collectively in spite of many differences in pose, lighting, body and object type. TCC embeddings encode the different phases of squatting without being provided explicit labels.
Right: Input videos of people performing a squat exercise. The video on the top left is the reference. The other videos show nearest neighbor frames (in the TCC embedding space) from other videos of people doing squats. Left: The corresponding frame embeddings move as the action is performed.
Applications of TCC
The learned per-frame embeddings enable an array of interesting applications:
  • Few-shot action phase classification
    When few labeled videos are available for training, the few-shot scenario, TCC performs very well. In fact, TCC can classify the phases of different actions with as few as a single labeled video. In the next figure we compare to other supervised and self-supervised learning approaches in the few-shot setting. We find that supervised learning requires about 50 videos with each frame labeled to achieve the same accuracy that self-supervised methods achieve with just one fully labeled video.
    Comparison of self-supervised and supervised learning for few-shot action phase classification.
  • Unsupervised video alignment
    Aligning or synchronizing videos manually becomes prohibitively difficult as the number of videos increases. Using TCC, many videos can be aligned by selecting the nearest neighbor to each frame in a reference video, without the need for additional labels, as demonstrated in the figure below.
    Results of unsupervised video alignment on videos of people pitching baseball using the distance between frames in the TCC space. The reference video used for alignment is shown in the upper left panel.
  • Label/modality transfer between videos
    Just as TCC finds similar frames by using a nearest neighbor search in the embedding space, it can transfer metadata associated with any frame in one video to its matching frame in another video. This metadata can be in the form of temporal semantic labels or other modalities, such as sound or text. In the video below we show two examples where we can transfer the sound of liquid being poured into a cup from one video to another.
  • Per-frame Retrieval
    With TCC, each frame in a video can be used as a query for retrieval of similar frames by looking up the nearest neighbors in the learned embedding space. The embeddings are powerful enough to differentiate between frames that look quite similar, such as frames just before or after the release of a bowling ball.
    We can perform retrieval from videos on a per-frame basis, i.e., any frame can be used to look up similar frames in a large collection of videos. The retrieved nearest neighbors show that the model captures fine-grained differences in the scene.
We are releasing our codebase, which includes implementations of a number of state-of-the-art self-supervised learning methods, including TCC. This codebase will be useful for researchers working on video understanding, as well as artists looking to use machine learning to align videos to create mosaics of people, animals, and objects moving synchronously.

This is joint work with Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. The authors would like to thank Alexandre Passos, Allen Lavoie, Anelia Angelova, Bryan Seybold, Priya Gupta, Relja Arandjelović, Sergio Guadarrama, Sourish Chaudhuri, and Vincent Vanhoucke for their help with this project. The videos used in this project come from the PennAction dataset. We thank the creators of PennAction for curating such an interesting dataset.

Source: Google AI Blog

Introducing the Indexing API and structured data for livestreams

Over the past few years, it's become easier than ever to stream live videos online, from celebrity updates to special events. But it's not always easy for people to determine which videos are live and know when to tune in.
Today, we're introducing new tools to help more people discover your livestreams in Search and Assistant. With livestream structured data and the Indexing API, you can let Google know when your video is live, so it will be eligible to appear with a red "live" badge:

Add livestream structured data to your page

If your website streams live videos, use the livestream developer documentation to flag your video as a live broadcast and mark the start and end times. In addition, VideoObject structured data is required to tell Google that there's a video on your page.

Update Google quickly with the Indexing API

The Indexing API now supports pages with livestream structured data. We encourage you to call the Indexing API to request that your site is crawled in time for the livestream. We recommend calling the Indexing API when your livestream begins and ends, and if the structured data changes.
For more information, visit our developer documentation. If you have any questions, ask us in the Webmaster Help Forum. We look forward to seeing your live videos on Google!

Changes to the URL Performance Report for YouTube video placements

What's changing?
The URL_PERFORMANCE_REPORT in the AdWords API will exclude information for YouTube video placements starting October 30, 2018, in keeping with our data retention policies. As a result, placements where the Url field has a domain of www.youtube.com will no longer appear in the report. New and improved placement reports will be available in one of the upcoming releases of the new Google Ads API.

What you should do
Review your application and workflows and make the necessary changes to ensure that the exclusion of video placements in this report will not cause problems. Watch this blog for updates regarding new placement reports in the Google Ads API.

If you have any questions or need help, please contact us via the forum.