# Learning Anthropometry from Rendered Humans

arXiv: https://arxiv.org/abs/2101.02515 · HTML: https://mlsanity.com/paper/2101.02515/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Learning to Estimate 3D Human Pose and Shape from a Single Color Image](https://mlsanity.com/paper/1805.04092/) (2018)
- [Unite the People: Closing the Loop Between 3D and 2D Human Representations](https://mlsanity.com/paper/1701.02468/) (2017)
- [DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation](https://mlsanity.com/paper/1511.06645/) (2015)
- [Building Statistical Shape Spaces for 3D Human Modeling](https://mlsanity.com/paper/1503.05860/) (2015)
- [Anthropometric clothing measurements from 3D body scans](https://mlsanity.com/paper/1911.00694/) (2019)
- [Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation](https://mlsanity.com/paper/1510.08291/) (2015)
- [Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images](https://mlsanity.com/paper/1806.08485/) (2018)

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

- [Automatic Estimation of Anthropometric Human Body Measurements](https://mlsanity.com/paper/2112.11992/) (2021)
- [Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation](https://mlsanity.com/paper/2205.12028/) (2022)
- [Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes](https://mlsanity.com/paper/2211.14822/) (2022)
- [Leveraging Digital Perceptual Technologies for Remote Perception and Analysis of Human Biomechanical Processes: A Contactless Approach for Workload and Joint Force Assessment](https://mlsanity.com/paper/2404.01576/) (2024)
- [ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training](https://mlsanity.com/paper/2508.01850/) (2025)

## Abstract (arXiv)

Accurate estimation of anthropometric body measurements from RGB images has many potential applications in industrial design, online clothing, medical diagnosis and ergonomics. Research on this topic is limited by the fact that there exist only generated datasets which are based on fitting a 3D body mesh to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are generated. To circumvent the data bottleneck, we introduce a new 3D scan dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset of 200 RGB images and tape measured ground truth. With the help of the two new datasets we propose a part-based shape model and a deep neural network for estimating anthropometric measurements from 2D images. All data will be made publicly available.