# PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers

arXiv: https://arxiv.org/abs/2303.09187 · HTML: https://mlsanity.com/paper/2303.09187/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 60 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 30 of 44)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [Learning to Estimate 3D Human Pose and Shape from a Single Color Image](https://mlsanity.com/paper/1805.04092/) (2018)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [PoseTrack: A Benchmark for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1710.10000/) (2017)
- [Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video](https://mlsanity.com/paper/2011.08627/) (2020)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021)
- [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1907.11346/) (2019)
- [3D Human Motion Estimation via Motion Compression and Refinement](https://mlsanity.com/paper/2008.03789/) (2020)

## Built on by (most cited first, 30 of 32)

- [Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot](https://mlsanity.com/paper/2402.14654/) (2024)
- [ChatPose: Chatting about 3D Human Pose](https://mlsanity.com/paper/2311.18836/) (2023)
- [Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning](https://mlsanity.com/paper/2404.05218/) (2024)
- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos](https://mlsanity.com/paper/2404.04430/) (2024)
- [Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption](https://mlsanity.com/paper/2404.11291/) (2024)
- [SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens](https://mlsanity.com/paper/2411.19824/) (2024)
- [DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency](https://mlsanity.com/paper/2408.07481/) (2024)
- [Text2Interact: High-Fidelity and Diverse Text-to-Two-Person Interaction Generation](https://mlsanity.com/paper/2510.06504/) (2025)
- [OmniCT: Towards a Unified Slice-Volume LVLM for Comprehensive CT Analysis](https://mlsanity.com/paper/2602.16110/) (2026)
- [Reconstructing Close Human Interactions from Multiple Views](https://mlsanity.com/paper/2401.16173/) (2024)
- [Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation](https://mlsanity.com/paper/2306.17074/) (2023)
- [Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery](https://mlsanity.com/paper/2403.09063/) (2024)
- [CondiMen: Conditional Multi-Person Mesh Recovery](https://mlsanity.com/paper/2412.13058/) (2024)
- [EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation](https://mlsanity.com/paper/2403.18080/) (2024)
- [Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes](https://mlsanity.com/paper/2408.05936/) (2024)
- [Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning](https://mlsanity.com/paper/2507.02565/) (2025)
- [DyCrowd: Towards Dynamic Crowd Reconstruction from a Large-scene Video](https://mlsanity.com/paper/2508.12644/) (2025)
- [Towards Metric-Aware Multi-Person Mesh Recovery by Jointly Optimizing Human Crowd in Camera Space](https://mlsanity.com/paper/2511.13282/) (2025)
- [PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery](https://mlsanity.com/paper/2403.12473/) (2024)
- [Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes](https://mlsanity.com/paper/2409.17671/) (2024)
- [HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery](https://mlsanity.com/paper/2412.04456/) (2024)
- [Multi-HMR 2: Multi-Person Camera-Centric Human Detection, Mesh Recovery and Tracking](https://mlsanity.com/paper/2606.14841/) (2026)
- [MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field](https://mlsanity.com/paper/2309.13607/) (2023)
- [Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations](https://mlsanity.com/paper/2504.09953/) (2025)
- [Self-Intersection-Aware 3D Human Motion Generation Using an Efficient Human Sphere Proxy](https://mlsanity.com/paper/2605.26744/) (2026)
- [On the Use of Hierarchical Vision Foundation Models for Low-Cost Human Mesh Recovery and Pose Estimation](https://mlsanity.com/paper/2510.12660/) (2025)
- [Discriminative-Generative Synergy for Occlusion Robust 3D Human Mesh Recovery](https://mlsanity.com/paper/2604.21712/) (2026)
- [InterMesh: Explicit Interaction-Aware End-to-End Multi-Person Human Mesh Recovery](https://mlsanity.com/paper/2605.04554/) (2026)
- [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026)

## Abstract (arXiv)

Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In this paper, we propose a new end-to-end multi-person 3D Pose and Shape estimation framework with progressive Video Transformer, termed PSVT. In PSVT, a spatio-temporal encoder (STE) captures the global feature dependencies among spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder (STSD) capture the global dependencies between pose queries and feature tokens, shape queries and feature tokens, respectively. To handle the variances of objects as time proceeds, a novel scheme of progressive decoding is used to update pose and shape queries at each frame. Besides, we propose a novel pose-guided attention (PGA) for shape decoder to better predict shape parameters. The two components strengthen the decoder of PSVT to improve performance. Extensive experiments on the four datasets show that PSVT achieves stage-of-the-art results.