# Multi-view Human Pose and Shape Estimation Using Learnable Volumetric Aggregation

arXiv: https://arxiv.org/abs/2011.13427 · HTML: https://mlsanity.com/paper/2011.13427/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Learnable human mesh triangulation for 3D human pose and shape estimation](https://mlsanity.com/paper/2208.11251/) (2022)
- [Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recovery](https://mlsanity.com/paper/2301.06020/) (2023)
- [Human Mesh Recovery from Arbitrary Multi-view Images](https://mlsanity.com/paper/2403.12434/) (2024)
- [Representation learning of vertex heatmaps for 3D human mesh reconstruction from multi-view images](https://mlsanity.com/paper/2306.16615/) (2023)
- [SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers](https://mlsanity.com/paper/2404.12625/) (2024)
- [HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery](https://mlsanity.com/paper/2412.04456/) (2024)
- [Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive Losses](https://mlsanity.com/paper/2402.02074/) (2024)
- [Self-learning Canonical Space for Multi-view 3D Human Pose Estimation](https://mlsanity.com/paper/2403.12440/) (2024)
- [Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset](https://mlsanity.com/paper/2512.10321/) (2025)
- [Monocular Models are Strong Learners for Multi-View Human Mesh Recovery](https://mlsanity.com/paper/2603.20391/) (2026)

## Abstract (arXiv)

Human pose and shape estimation from RGB images is a highly sought after alternative to marker-based motion capture, which is laborious, requires expensive equipment, and constrains capture to laboratory environments. Monocular vision-based algorithms, however, still suffer from rotational ambiguities and are not ready for translation in healthcare applications, where high accuracy is paramount. While fusion of data from multiple viewpoints could overcome these challenges, current algorithms require further improvement to obtain clinically acceptable accuracies. In this paper, we propose a learnable volumetric aggregation approach to reconstruct 3D human body pose and shape from calibrated multi-view images. We use a parametric representation of the human body, which makes our approach directly applicable to medical applications. Compared to previous approaches, our framework shows higher accuracy and greater promise for real-time prediction, given its cost efficiency.