# InfiniteForm: A synthetic, minimal bias dataset for fitness applications

arXiv: https://arxiv.org/abs/2110.01330 · HTML: https://mlsanity.com/paper/2110.01330/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Yoga-82: A New Dataset for Fine-grained Classification of Human Poses](https://mlsanity.com/paper/2004.10362/) (2020)
- [Pose Trainer: Correcting Exercise Posture using Pose Estimation](https://mlsanity.com/paper/2006.11718/) (2020)
- [Recognizing Exercises and Counting Repetitions in Real Time](https://mlsanity.com/paper/2005.03194/) (2020)

## Built on by (most cited first, 5 of 5)

- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [Accurate 3D Body Shape Regression using Metric and Semantic Attributes](https://mlsanity.com/paper/2206.07036/) (2022)
- [OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics](https://mlsanity.com/paper/2406.09788/) (2024)
- [ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation](https://mlsanity.com/paper/2403.01345/) (2024)
- [Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles](https://mlsanity.com/paper/2304.14489/) (2023)

## Abstract (arXiv)

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.