InfiniteForm: A synthetic, minimal bias dataset for fitness applications

TL;DR

InfiniteForm is a large, diverse synthetic dataset designed to improve fitness pose estimation models by providing minimal bias, detailed annotations, and a novel pose generation method for training robust computer vision systems.

Contribution

The paper introduces InfiniteForm, a synthetic fitness dataset with diverse poses and annotations, and a new generative process for creating varied synthetic fitness poses.

Findings

InfiniteForm contains 60,000 images with diverse fitness poses.

The dataset provides pixel-perfect labels including depth and occlusion.

The generative process enhances pose diversity for training robust models.

Abstract

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.