# Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation

arXiv: https://arxiv.org/abs/2205.12028 · HTML: https://mlsanity.com/paper/2205.12028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 6 of 6)

- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Anthropometric clothing measurements from 3D body scans](https://mlsanity.com/paper/1911.00694/) (2019)
- [Learning Anthropometry from Rendered Humans](https://mlsanity.com/paper/2101.02515/) (2021)
- [Automatic Estimation of Anthropometric Human Body Measurements](https://mlsanity.com/paper/2112.11992/) (2021)
- [A Neural Anthropometer Learning from Body Dimensions Computed on Human 3D Meshes](https://mlsanity.com/paper/2110.04064/) (2021)
- [Human biases in body measurement estimation](https://mlsanity.com/paper/2009.07828/) (2020)

## Abstract (arXiv)

Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE.