Human Mesh Recovery

Also known as HMR

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 38

All topics

GRAFT: Geometric Refinement and Fitting Transformer for Human Scene Reconstruction
Apr 21, 2026Pradyumna YM, Yuxuan Xue, Yue Chen +33D ViTs 3D Reconstruction
University of Tübingen · Tübingen AI Center · Westlake University +1
Discriminative-Generative Synergy for Occlusion Robust 3D Human Mesh Recovery
Apr 20, 2026Yang Liu, Zhiyong Zhang3D Reconstruction Human Mesh Recovery
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China
DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
Apr 19, 2026Rongxiao Guo, Qingchao Chen3D Reconstruction Human Mesh Recovery
Institute of Medical Technology, Peking University Health Science Center · National Institute of Health Data Science at Peking University · State Key Laboratory of General Artificial Intelligence, Peking University
SKEL-CF: Coarse-to-Fine Biomechanical Skeleton and Surface Mesh Recovery
Nov 25, 2025Da Li, Jiping Jin, Xuanlong Yu +63D Reconstruction Human Mesh Recovery
1Intellindust AI Lab · 3ShanghaiTech University · 5Didi Chuxing Co.Ltd +1
MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images
Jun 11, 2025Chentao Song, He Zhang, Haolei Yuan +4Metric Depth Estimation 3D Reconstruction
Tsinghua University · Beihang University · Beijing Normal University
Joint Optimization for 4D Human-Scene Reconstruction in the Wild
Jan 4, 2025Zhizheng Liu, Joe Lin, Wayne Wu +1Markerless Motion Capture 4D Reconstruction
Department of Computer Science, University of California, Los Angeles
Sonicmesh: Enhancing 3D Human Mesh Reconstruction in Vision-Impaired Environments With Acoustic Signals
Dec 15, 2024Xiaoxuan Liang, Hong Zhou, Zhaolong Wei +33D Reconstruction Human Mesh Recovery
University of Massachusetts at Amherst, Location, Country · Trine University · University of Illinois at Chicago +1
Multi-view Hand Reconstruction with a Point-Embedded Transformer
Aug 20, 2024Lixin Yang, Licheng Zhong, Pengxiang Zhu +43D ViTs Markerless Motion Capture
School of Artificial Intelligence (SAI), Shanghai Jiao Tong University, Shanghai 200230, China · School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China +1

Apr 21, 2026, cs.CVJ/K move · Enter open · S save

GRAFT: Geometric Refinement and Fitting Transformer for Human Scene Reconstruction

Pradyumna YM, Yuxuan Xue, Yue Chen, Nikita Kister+2

University of Tübingen · Tübingen AI Center · Westlake University+1

3D ViTs 3D Reconstruction Human Mesh Recovery 3D Mesh Reconstruction 3D Human Reconstruction Feed-Forward 3D Reconstruction

Open paper AskPDF arXiv

Reconstructing physically plausible 3D human-scene interactions (HSI) from a single image currently presents a trade-off: optimization based methods offer accurate contact but are slow (~20s), while feed-forward approaches are fast yet lack explicit interaction reasoning, producing floating and interpenetration artifacts. Our key insight is that geometry-based human--scene fitting can be amortized into fast feed-forward inference. We present GRAFT (Geometric Refinement And Fitting Transformer), a learned HSI prior that predicts Interaction Gradients: corrective parameter updates that iteratively refine human meshes by reasoning about their 3D relationship to the surrounding scene. GRAFT encodes the interaction state into compact body-anchored tokens, each grounded in the scene geometry via Geometric Probes that capture spatial relationships with nearby surfaces. A lightweight transformer recurrently updates human meshes and re-probes the scene, ensuring the final pose aligns with both learned priors and observed geometry. GRAFT operates either as an end-to-end reconstructor using image features, or with geometry alone as a transferable plug-and-play HSI prior that improves feed-forward methods without retraining. Experiments show GRAFT improves interaction quality by up to 122% over state-of-the-art feed-forward methods and matches optimization-based interaction quality at

{\sim}100{\times}

lower runtime, while generalizing seamlessly to in-the-wild multi-person scenes and being preferred in 64.8% of three-way user study. Project page: https://pradyumnaym.github.io/graft .

Momentum

Related topics

Latest papers 38