# CALVIS: chest, waist and pelvis circumference from 3D human body meshes as ground truth for deep learning

arXiv: https://arxiv.org/abs/2003.00834 · HTML: https://mlsanity.com/paper/2003.00834/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [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)
- [Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images](https://mlsanity.com/paper/2205.14347/) (2022)
- [Pose-independent 3D Anthropometry from Sparse Data](https://mlsanity.com/paper/2501.06014/) (2025)

## Abstract (arXiv)

In this paper we present CALVIS, a method to calculate Chest, wAist and peLVIS circumference from 3D human body meshes. Our motivation is to use this data as ground truth for training convolutional neural networks (CNN). Previous work had used the large scale CAESAR dataset or determined these anthropometrical measurements textitmanually from a person or human 3D body meshes. Unfortunately, acquiring these data is a cost and time consuming endeavor. In contrast, our method can be used on 3D meshes automatically. We synthesize eight human body meshes and apply CALVIS to calculate chest, waist and pelvis circumference. We evaluate the results qualitatively and observe that the measurements can indeed be used to estimate the shape of a person. We then asses the plausibility of our approach by generating ground truth with CALVIS to train a small CNN. After having trained the network with our data, we achieve competitive validation error. Furthermore, we make the implementation of CALVIS publicly available to advance the field.