InfiniteForm: A synthetic, minimal bias dataset for fitness applications
This addresses the problem of inaccurate pose tracking in remote fitness applications for developers and researchers, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the domain gap in fitness-specific computer vision by creating InfiniteForm, a synthetic dataset of 60k images with diverse fitness poses and realistic variations, which provides pixel-perfect labels for annotations like 2D keypoints, depth, and occlusion.
The growing popularity of remote fitness has increased the demand for highly accurate computer vision models that track human poses. However, the best methods still fail in many real-world fitness scenarios, suggesting that there is a domain gap between current datasets and real-world fitness data. To enable the field to address fitness-specific vision problems, we created InfiniteForm, an open-source synthetic dataset of 60k images with diverse fitness poses (15 categories), both single- and multi-person scenes, and realistic variation in lighting, camera angles, and occlusions. As a synthetic dataset, InfiniteForm offers minimal bias in body shape and skin tone, and provides pixel-perfect labels for standard annotations like 2D keypoints, as well as those that are difficult or impossible for humans to produce like depth and occlusion. In addition, we introduce a novel generative procedure for creating diverse synthetic poses from predefined exercise categories. This generative process can be extended to any application where pose diversity is needed to train robust computer vision models.