![]() In the video below, you can see how the model detects the spine angle on an example video. To measure the angle, we treated the spine (start keypoint 0, end keypoint 8) as a vector and measured the angle between the vector and the XY plane by taking arccos of cosine similarity equation. Keeping the back straight is important in this exercise since the more you lean forward, the more the center of mass (body + barbell) is shifted forward (it can be a very bad thing since the shifted center of mass causes extra load on the spine). The spine angle is one of the most important things to be analyzed when squatting. In order to explore the capabilities and limitations of the VideoPose3D, we applied it for analysis of powerlifting and martial arts exercises. By utilizing the information from multiple frames taken at different periods, VideoPose3D essentially makes a prediction based on the data about the past and the future position of joints, which allows a more accurate prediction of the current joints’ state and partial resolution of the uncertainty issues (for example, when the joint is occluded in one of the frames, the model can “look” at the neighbouring frames to resolve the problem). (credit Pavllo et al.)Īs an input, this model requires a set of 2D keypoint detections, where the 2D detector is pre-trained on the COCO 2017 dataset. Note that in this illustration both past and future frames are used to make a prediction. VideoPose3D belongs to a convolutional neural networks (CNNs) family and employs dilated temporal convolutions (see the illustration below).ĢD to 3D keypoints transfer using VideoPose3D. Thereby, we chose the VideoPose3D model since it works with simple single-view detections. It is the case when complicated multi-camera setup and depth sensors are not available. In this scenario, users should capture themselves while doing an exercise, analyze how correct it is performed by using the app, and review the mistakes made during the exercise performance. We took a realistic situation of application development for an AI fitness coach. How Human Pose Estimation Model Detects and Analyze Movements For example, in the Human Pose as Calibration Pattern paper is described that the initial detections from uncalibrated cameras can be optimized using the external knowledge on the natural proportions of the human body and relaxed reprojection error to obtain the final 3D prediction. However, some authors demonstrate that even the video stream from multiple unsynchronized and uncalibrated cameras can be used to estimate 3D joint positions. Normally this method requires cameras to be synchronized. As a result, predictions of models become more accurate. The multi-view technique allows for improved depth perception and helps in those cases when some parts of the body are occluded in the image. Multi-camera model pose estimation – multiple 2d detections are combined to predict the final 3D pose (credit – Learnable Triangulation of Human Pose, ) Alternatively, it’s possible to exploit multi-view image data where every frame is captured from several cameras focused on the target scene from different angles. Regardless of the approach (image →2D →3D or image → 3D), 3D keypoints are typically inferred using single-view images. Moreover, many existing models provide decent accuracy and real-time inference speed (for example, PoseNet, HRNet, Mask R-CNN, Cascaded Pyramid Network). This approach is the most common because 2D keypoint prediction is well-explored and usage of a pre-trained backbone for 2D predictions increases the overall accuracy of the system. To detect the 2D keypoints and then transform them into 3D.It is helpful since a common problem with training 3D human pose estimation models is a lack of high-quality 3D pose annotations. Instead, it constructs the 3D ground truth in a self-supervised way by applying epipolar geometry to 2D predictions. The interesting thing is that it requires no ground truth 3D data for training - only 2D keypoints. To train a model capable of inferring 3D keypoints directly from the provided images.įor example, a multi-view model EpipolarPose is trained to jointly estimate the positions of 2D and 3D keypoints.There are multiple approaches to 3D human pose estimation: When keypoints are extracted from a sequence of frames of a video stream, the system can analyze the person’s actual movement. Once the position of joints is extracted, the movement analysis system checks the posture of a person. ![]()
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