# Multiple View Geometry Transformers for 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/2311.10983 · HTML: https://mlsanity.com/paper/2311.10983/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 27 of 27)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Embracing Single Stride 3D Object Detector with Sparse Transformer](https://mlsanity.com/paper/2112.06375/) (2021)
- [BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving](https://mlsanity.com/paper/2205.09743/) (2022)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)
- [Populating 3D Scenes by Learning Human-Scene Interaction](https://mlsanity.com/paper/2012.11581/) (2020)
- [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019)
- [Ordinal Depth Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/1805.04095/) (2018)
- [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2203.10314/) (2022)
- [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://mlsanity.com/paper/1901.04111/) (2019)
- [Direct Multi-view Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2111.04076/) (2021)
- [4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras](https://mlsanity.com/paper/2002.12625/) (2020)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo](https://mlsanity.com/paper/2104.02273/) (2021)
- [Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images](https://mlsanity.com/paper/2109.05885/) (2021)
- [Generalizable Human Pose Triangulation](https://mlsanity.com/paper/2110.00280/) (2021)
- [VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2205.12602/) (2022)

## Built on by (most cited first, 19 of 19)

- [MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation](https://mlsanity.com/paper/2509.00649/) (2025)
- [An uncertainty-aware framework for data-efficient multi-view animal pose estimation](https://mlsanity.com/paper/2510.09903/) (2025)
- [RUMPL: Ray-Based Transformers for Universal Multi-View 2D to 3D Human Pose Lifting](https://mlsanity.com/paper/2512.15488/) (2025)
- [Multi-person Physics-based Pose Estimation for Combat Sports](https://mlsanity.com/paper/2504.08175/) (2025)
- [Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization](https://mlsanity.com/paper/2512.05259/) (2025)
- [COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimation](https://mlsanity.com/paper/2601.09698/) (2026)
- [Unconstrained Multi-view Human Pose Estimation with Algebraic Priors](https://mlsanity.com/paper/2604.24312/) (2026)
- [MAMMA: Markerless & Automatic Multi-Person Motion Action Capture](https://mlsanity.com/paper/2506.13040/) (2025)
- [Fast Neural Inverse Kinematics on Human Body Motions](https://mlsanity.com/paper/2506.17996/) (2025)
- [Unsupervised Monocular 3D Keypoint Discovery from Multi-View Diffusion Priors](https://mlsanity.com/paper/2507.12336/) (2025)
- [3D Ground Truth Reconstruction from Multi-Camera Annotations Using UKF](https://mlsanity.com/paper/2511.17609/) (2025)
- [Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser](https://mlsanity.com/paper/2601.01360/) (2026)
- [Monocular Models are Strong Learners for Multi-View Human Mesh Recovery](https://mlsanity.com/paper/2603.20391/) (2026)
- [Efficient Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation](https://mlsanity.com/paper/2604.16522/) (2026)
- [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026)
- [DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation](https://mlsanity.com/paper/2606.07419/) (2026)
- [Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT](https://mlsanity.com/paper/2609.22291/) (2026)
- [STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation](https://mlsanity.com/paper/2609.24482/) (2026)
- [ESTHER: Egocentric Stereo Hand Estimation and Reconstruction in the Wild](https://mlsanity.com/paper/2609.34817/) (2026)

## Abstract (arXiv)

In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation. Recent works have focused on end-to-end learning-based transformer designs, which struggle to resolve geometric information accurately, particularly during occlusion. Instead, we propose a novel hybrid model, MVGFormer, which has a series of geometric and appearance modules organized in an iterative manner. The geometry modules are learning-free and handle all viewpoint-dependent 3D tasks geometrically which notably improves the model's generalization ability. The appearance modules are learnable and are dedicated to estimating 2D poses from image signals end-to-end which enables them to achieve accurate estimates even when occlusion occurs, leading to a model that is both accurate and generalizable to new cameras and geometries. We evaluate our approach for both in-domain and out-of-domain settings, where our model consistently outperforms state-of-the-art methods, and especially does so by a significant margin in the out-of-domain setting. We will release the code and models: https://github.com/XunshanMan/MVGFormer.