The state of the art in machine learning (ML) has achieved exceptional accuracy on many computer vision tasks solely by training models on photos. Building upon these successes and advancing 3D object understanding has great potential to power a wider range of applications, such as augmented reality, robotics, autonomy, and image retrieval. For example, earlier this year we released MediaPipe Objectron, a set of real-time 3D object detection models designed for mobile devices, which were trained on a fully annotated, real-world 3D dataset, that can predict objects’ 3D bounding boxes.

Yet, understanding objects in 3D remains a challenging task due to the lack of large real-world datasets compared to 2D tasks (e.g., ImageNet, COCO, and Open Images). To empower the research community for continued advancement in 3D object understanding, there is a strong need for the release of object-centric video datasets, which capture more of the 3D structure of an object, while matching the data format used for many vision tasks (i.e., video or camera streams), to aid in the training and benchmarking of machine learning models.

Today, we are excited to release the Objectron dataset, a collection of short, object-centric video clips capturing a larger set of common objects from different angles. Each video clip is accompanied by AR session metadata that includes camera poses and sparse point-clouds. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images collected from a geo-diverse sample (covering 10 countries across five continents).

Example videos in the Objectron dataset.

A 3D Object Detection Solution
Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. These models are released in MediaPipe, Google's open source framework for cross-platform customizable ML solutions for live and streaming media, which also powers ML solutions like on-device real-time hand, iris and body pose tracking.

Sample results of 3D object detection solution running on mobile.

In contrast to the previously released single-stage Objectron model, these newest versions utilize a two-stage architecture. The first stage employs the TensorFlow Object Detection model to find the 2D crop of the object. The second stage then uses the image crop to estimate the 3D bounding box while simultaneously computing the 2D crop of the object for the next frame, so that the object detector does not need to run every frame. The second stage 3D bounding box predictor runs at 83 FPS on Adreno 650 mobile GPU.

Diagram of a reference 3D object detection solution.

Evaluation Metric for 3D Object Detection
With ground truth annotations, we evaluate the performance of 3D object detection models using 3D intersection over union (IoU) similarity statistics, a commonly used metric for computer vision tasks, which measures how close the bounding boxes are to the ground truth.

We propose an algorithm for computing accurate 3D IoU values for general 3D-oriented boxes. First, we compute the intersection points between faces of the two boxes using Sutherland-Hodgman Polygon clipping algorithm. This is similar to frustum culling, a technique used in computer graphics. The volume of the intersection is computed by the convex hull of all the clipped polygons. Finally, the IoU is computed from the volume of the intersection and volume of the union of two boxes. We are releasing the evaluation metrics source code along with the dataset.

Compute the 3D intersection over union using the polygon clipping algorithm, Left: Compute the intersection points of each face by clipping the polygon against the box. Right: Compute the volume of intersection by computing the convex hull of all intersection points (green).

Dataset Format
The technical details of the Objectron dataset, including usage and tutorials, are available on the dataset website. The dataset includes bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes, and is stored in the objectron bucket on Google Cloud storage with the following assets:

• The video sequences
• The annotation labels (3D bounding boxes for objects)
• AR metadata (such as camera poses, point clouds, and planar surfaces)
• Processed dataset: shuffled version of the annotated frames, in tf.example format for images and SequenceExample format for videos.
• Supporting scripts to run evaluation based on the metric described above
• Supporting scripts to load the data into Tensorflow, PyTorch, and Jax and to visualize the dataset, including “Hello World” examples

With the dataset, we are also open-sourcing a data-pipeline to parse the dataset in popular Tensorflow, PyTorch and Jax frameworks. Example colab notebooks are also provided.

By releasing this Objectron dataset, we hope to enable the research community to push the limits of 3D object geometry understanding. We also hope to foster new research and applications, such as view synthesis, improved 3D representation, and unsupervised learning. Stay tuned for future activities and developments by joining our mailing list and visiting our github page.

Acknowledgements
The research described in this post was done by Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Mogan Shieh, Ryan Hickman, Buck Bourdon, Alexander Kanaukou, Chuo-Ling Chang, Matthias Grundmann, ‎and Tom Funkhouser. We thank Aliaksandr Shyrokau, Sviatlana Mialik, Anna Eliseeva, and the annotation team for their high quality annotations. We also would like to thank Jonathan Huang and Vivek Rathod for their guidance on TensorFlow Object Detection API.

Pose estimation from video plays a critical role enabling the overlay of digital content and information on top of the physical world in augmented reality, sign language recognition, full-body gesture control, and even quantifying physical exercises, where it can form the basis for yoga, dance, and fitness applications. Pose estimation for fitness applications is particularly challenging due to the wide variety of possible poses (e.g., hundreds of yoga asanas), numerous degrees of freedom, occlusions (e.g. the body or other objects occlude limbs as seen from the camera), and a variety of appearances or outfits.

Today we are announcing the release of a new approach to human body pose perception, BlazePose, which we presented at the CV4ARVR workshop at CVPR 2020. Our approach provides human pose tracking by employing machine learning (ML) to infer 33, 2D landmarks of a body from a single frame. In contrast to current pose models based on the standard COCO topology, BlazePose accurately localizes more keypoints, making it uniquely suited for fitness applications. In addition, current state-of-the-art approaches rely primarily on powerful desktop environments for inference, whereas our method achieves real-time performance on mobile phones with CPU inference. If one leverages GPU inference, BlazePose achieves super-real-time performance, enabling it to run subsequent ML models, like face or hand tracking.

Topology
The current standard for human body pose is the COCO topology, which consists of 17 landmarks across the torso, arms, legs, and face. However, the COCO keypoints only localize to the ankle and wrist points, lacking scale and orientation information for hands and feet, which is vital for practical applications like fitness and dance. The inclusion of more keypoints is crucial for the subsequent application of domain-specific pose estimation models, like those for hands, face, or feet.

With BlazePose, we present a new topology of 33 human body keypoints, which is a superset of COCO, BlazeFace and BlazePalm topologies. This allows us to determine body semantics from pose prediction alone that is consistent with face and hand models.

BlazePose 33 keypoint topology as COCO (colored with green) superset

Overview: An ML Pipeline for Pose Tracking
For pose estimation, we utilize our proven two-step detector-tracker ML pipeline. Using a detector, this pipeline first locates the pose region-of-interest (ROI) within the frame. The tracker subsequently predicts all 33 pose keypoints from this ROI. Note that for video use cases, the detector is run only on the first frame. For subsequent frames we derive the ROI from the previous frame’s pose keypoints as discussed below.

Human pose estimation pipeline overview.

Pose Detection by extending BlazeFace
For real-time performance of the full ML pipeline consisting of pose detection and tracking models, each component must be very fast, using only a few milliseconds per frame. To accomplish this, we observe that the strongest signal to the neural network about the position of the torso is the person's face (due to its high-contrast features and comparably small variations in appearance). Therefore, we achieve a fast and lightweight pose detector by making the strong (yet for many mobile and web applications valid) assumption that the head should be visible for our single-person use case.

Consequently, we trained a face detector, inspired by our sub-millisecond BlazeFace model, as a proxy for a pose detector. Note, this model only detects the location of a person within the frame and can not be used to identify individuals. In contrast to the Face Mesh and MediaPipe Hand tracking pipelines, where we derive the ROI from predicted keypoints, for the human pose tracking we explicitly predict two additional virtual keypoints that firmly describe the human body center, rotation and scale as a circle. Inspired by Leonardo’s Vitruvian man, we predict the midpoint of a person's hips, the radius of a circle circumscribing the whole person, and the incline angle of the line connecting the shoulder and hip midpoints. This results in consistent tracking even for very complicated cases, like specific yoga asanas. The figure below illustrates the approach.

Vitruvian man aligned via two virtual keypoints predicted by our BlazePose detector in addition to the face bounding box

Tracking Model
The pose estimation component of the pipeline predicts the location of all 33 person keypoints with three degrees of freedom each (x, y location and visibility) plus the two virtual alignment keypoints described above. Unlike current approaches that employ compute-intensive heatmap prediction, our model uses a regression approach that is supervised by a combined heat map/offset prediction of all keypoints, as shown below.

Tracking network architecture: regression with heatmap supervision

Specifically, during training we first employ a heatmap and offset loss to train the center and left tower of the network. We then remove the heatmap output and train the regression encoder (right tower), thus, effectively using the heatmap to supervise a lightweight embedding.

The table below shows an ablation study of the model quality resulting from different training strategies. As an evaluation metric, we use the Percent of Correct Points with 20% tolerance ([email protected]) (where we assume the point to be detected correctly if the 2D Euclidean error is smaller than 20% of the corresponding person’s torso size). To obtain a human baseline, we asked annotators to annotate several samples redundantly and obtained an average [email protected] of 97.2. The training and validation have been done on a geo-diverse dataset of various poses, sampled uniformly.

To cover a wide range of customer hardware, we present two pose tracking models: lite and full, which are differentiated in the balance of speed versus quality. For performance evaluation on CPU, we use XNNPACK; for mobile GPUs, we use the TFLite GPU backend.

Applications
Based on human pose, we can build a variety of applications, like fitness or yoga trackers. As an example, we present squats and push up counters, which can automatically count user statistics, or verify the quality of performed exercises. Such use cases can be implemented either using an additional classifier network or even with a simple joint pairwise distance lookup algorithm, which matches the closest pose in normalized pose space.

The number of performed exercises counter based on detected body pose. Left: Squats; Right: Push-Ups

Conclusion
BlazePose will be available to the broader mobile developer community via the Pose detection API in the upcoming release of ML Kit, and we are also releasing a version targeting upper body use cases in MediaPipe running in Android, iOS and Python. Apart from the mobile domain, we preview our web-based in-browser version as well. We hope that providing this human pose perception functionality to the broader research and development community will result in an emergence of creative use cases, stimulating new applications, and new research avenues.

We plan to extend this technology with more robust and stable tracking to an even larger variety of human poses and activities. In the accompanying Model Card, we detail the intended uses, limitations and model fairness to ensure that use of these models aligns with Google’s AI Principles. We believe that publishing this technology can provide an impulse to new creative ideas and applications by the members of the research and developer community at large. We are excited to see what you can build with it!

Acknowledgments
Special thanks to all our team members who worked on the tech with us: Fan Zhang, Artsiom Ablavatski, Yury Kartynnik, Tyler Zhu, Karthik Raveendran, Andrei Vakunov, Andrei Tkachenka, Marat Dukhan, Tyler Mullen, Gregory Karpiak, Suril Shah, Buck Bourdon, Jiuqiang Tang, Ming Guang Yong, Chuo-Ling Chang, Esha Uboweja, Siarhei Kazakou, Andrei Kulik, Matsvei Zhdanovich, and Matthias Grundmann.

A wide range of real-world applications, including computational photography (e.g., portrait mode and glint reflections) and augmented reality effects (e.g., virtual avatars) rely on estimating eye position by tracking the iris. Once accurate iris tracking is available, we show that it is possible to determine the metric distance from the camera to the user — without the use of a dedicated depth sensor. This, in-turn, can improve a variety of use cases, ranging from computational photography, over virtual try-on of properly sized glasses and hats to usability enhancements that adopt the font size depending on the viewer’s distance.

Iris tracking is a challenging task to solve on mobile devices, due to limited computing resources, variable light conditions and the presence of occlusions, such as hair or people squinting. Often, sophisticated specialized hardware is employed, limiting the range of devices on which the solution could be applied.

FaceMesh can be adopted to drive virtual avatars (middle). By additionally employing iris tracking (right), the avatar’s liveliness is significantly enhanced.

An example of eye re-coloring enabled by MediaPipe Iris.

Today, we announce the release of MediaPipe Iris, a new machine learning model for accurate iris estimation. Building on our work on MediaPipe Face Mesh, this model is able to track landmarks involving the iris, pupil and the eye contours using a single RGB camera, in real-time, without the need for specialized hardware. Through use of iris landmarks, the model is also able to determine the metric distance between the subject and the camera with relative error less than 10% without the use of depth sensor. Note that iris tracking does not infer the location at which people are looking, nor does it provide any form of identity recognition. Thanks to the fact that this system is implemented in MediaPipe — an open source cross-platform framework for researchers and developers to build world-class ML solutions and applications — it can run on most modern mobile phones, desktops, laptops and even on the web.

Usability prototype for far-sighted individuals: observed font size remains constant independent of the device distance from the user.

An ML Pipeline for Iris Tracking
The first step in the pipeline leverages our previous work on 3D Face Meshes, which uses high-fidelity facial landmarks to generate a mesh of the approximate face geometry. From this mesh, we isolate the eye region in the original image for use in the iris tracking model. The problem is then divided into two parts: eye contour estimation and iris location. We designed a multi-task model consisting of a unified encoder with a separate component for each task, which allowed us to use task-specific training data.

Examples of iris (blue) and eyelid (red) tracking.

To train the model from the cropped eye region, we manually annotated ~50k images, representing a variety of illumination conditions and head poses from geographically diverse regions, as shown below.

Eye region annotated with eyelid (red) and iris (blue) contours.

Cropped eye regions form the input to the model, which predicts landmarks via separate components.

Depth-from-Iris: Depth Estimation from a Single Image
Our iris-tracking model is able to determine the metric distance of a subject to the camera with less than 10% error, without requiring any specialized hardware. This is done by relying on the fact that the horizontal iris diameter of the human eye remains roughly constant at 11.7±0.5 mm across a wide population [1, 2, 3, 4], along with some simple geometric arguments. For illustration, consider a pinhole camera model projecting onto a sensor of square pixels. The distance to a subject can be estimated from facial landmarks by using the focal length of the camera, which can be obtained using camera capture APIs or directly from the EXIF metadata of a captured image, along with other camera intrinsic parameters. Given the focal length, the distance from the subject to the camera is directly proportional to the physical size of the subject’s eye, as visualized below.

The distance of the subject (d) can be computed from the focal length (f) and the size of the iris using similar triangles.

Left: MediaPipe Iris predicting metric distance in cm on a Pixel 2 from iris tracking alone, without the use of a depth sensor. Right: Ground-truth depth.

In order to quantify the accuracy of the method, we compared it to the depth sensor on an iPhone 11 by collecting front-facing, synchronized video and depth images on over 200 participants. We experimentally verified the error of the iPhone 11 depth sensor to be < 2% for distances up to 2 meters, using a laser ranging device. Our evaluation shows that our approach for depth estimation using iris size has a mean relative error of 4.3% and standard deviation of 2.4%. We tested our approach on participants with and without eyeglasses (not accounting for contact lenses on participants) and found that eyeglasses increase the mean relative error slightly to 4.8% (standard deviation 3.1%). We did not test this approach on participants with any eye diseases (like arcus senilis or pannus). Considering MediaPipe Iris requires no specialized hardware, these results suggest it may be possible to obtain metric depth from a single image on devices with a wide range of cost-points.

Histogram of estimation errors (left) and comparison of actual to estimated distance by iris (right).

Release of MediaPipe Iris
We are releasing the iris and depth estimation models as a cross-platform MediaPipe pipeline that can run on desktop, mobile and the web. As described in our recent Google Developer Blog post on MediaPipe on the web, we leverage WebAssembly and XNNPACK to run our Iris ML pipeline locally in the browser, without any data being sent to the cloud.

Future Directions
We plan to extend our MediaPipe Iris model with even more stable tracking for lower error and deploy it for accessibility use cases. We strongly believe in sharing code that enables reproducible research, rapid experimentation, and development of new ideas in different areas. In our documentation and the accompanying Model Card, we detail the intended uses, limitations and model fairness to ensure that use of these models aligns with Google’s AI Principles. Note, that any form of surveillance or identification is explicitly out of scope and not enabled by this technology. We hope that providing this iris perception functionality to the wider research and development community will result in an emergence of creative use cases, stimulating responsible new applications and new research avenues.

For more ML solutions from MediaPipe, please see our solutions page and Google Developer blog for the latest updates.

Acknowledgements
We would like to thank Artsiom Ablavatski, Andrei Tkachenka, Buck Bourdon, Ivan Grishchenko and Gregory Karpiak for support in model evaluation and data collection; Yury Kartynnik, Valentin Bazarevsky, Artsiom Ablavatski for developing the mesh technology; Aliaksandr Shyrokau and the annotation team for their diligence to data preparation; Vidhya Navalpakkam, Tomer, Tomer Shekel, Kai Kohlhoff for their domain expertise, Fan Zhang, Esha Uboweja, Tyler Mullen, Michael Hays and Chuo-Ling Chang for help to integrate the model to MediaPipe; Matthias Grundmann, Florian Schroff and Ming Guang Yong for continuous help for building this technology.