UT‑HMR is a one‑stage monocular 3D human mesh recovery framework that introduces uncertainty‑guided temporal modeling to address ambiguities caused by weak observations such as distance, occlusion, and motion blur. It predicts a Gaussian distribution over SMPL pose with a low‑rank-plus‑diagonal covariance, converting this into joint‑wise precision weights for heteroscedastic supervision while keeping inference deterministic. Temporal early fusion incorporates neighboring‑frame evidence before pose decoding, enabling the model to resolve pose ambiguities and achieve state‑of‑the‑art results on VIP‑HockeyPose‑v2, WorldPose, and competitive performance on 3DPW using a lightweight ViT‑B backbone.
By Zhibo Wang, Sirisha Rambhatla, Yuhao Chen, Maria Koshkina, David Clausi
arXiv:2605.14854v3 Announce Type: replace-cross
Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evide...
By Patrick Kwon, Chen Chen
arXiv:2601.13913v3 Announce Type: replace
Abstract: We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2...
By Pavlo Melnyk, Cuong Le, Urs Waldmann, Per-Erik Forss\'en, Bastian Wandt
arXiv:2507. 12138v2 Announce Type: replace-cross Abstract: We introduce a principled, data-driven approach for modeling a neural prior over human body poses using normalizing flows.
By Michal Heker, Sefy Kagarlitsky, David Tolpin
arXiv:2607. 08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable.
By Ayda Eghbalian, Kevin Desai
Human mesh recovery (HMR) aims to recover 3D human meshes from images. Most existing HMR benchmarks and methods focus on either multi-person reconstruction from a single view or single-person reconstruction from multiple views, where the number of subjects and the scene scale are relatively limited.
arXiv:2509.04600v2 Announce Type: replace
Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
By Zhongyuan Hu, Qijun Ying, Jiazhi Shu, Ronghui Li, Yu Lu, Zijiao Zeng, Xiu Li
arXiv:2603.12789v3 Announce Type: replace
Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independ...
By Sangmin Kim, Minhyuk Hwang, Geonho Cha, Dongyoon Wee, Jaesik Park
The paper introduces a top‑down approach for multi‑person 3D reconstruction from multiple views, using a unified, instance‑centric human‑aware 3D space. Observations from different cameras are lifted into this shared space where geometry, appearance, and semantic cues are jointly encoded, and a spatial contrastive learning strategy aligns features of the same person across views while separating different individuals. The method then regresses SMPL parameters from 3D tokens in a feed‑forward manner, achieving robust, accurate, and efficient reconstruction even under severe occlusions.
By Yuanwang Yang, Buzhen Huang, Zongxuan Ren, Jing Huang, Kun Li
arXiv:2609.01276v1 Announce Type: new
Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
By Kai Guan, Minchao Jiang, Ruichen WangLi, Wentao Zhu, Lei Zhang
Accurate monocular 4D hand reconstruction remains challenging. Per-frame discriminative regressors lack temporal context and often produce jittery predictions.
DirtyMoCap is a marker‑layout‑free framework that converts unordered, noisy optical motion capture markers into a fixed set of proxy anchors representing skeletal joints and body surface points. Using a recurrent sliding‑window architecture to track these anchors and a custom differentiable Gauss‑Newton solver to fit the SMPL‑H model, the method learns adaptive observation confidence, smoothness, and prior weights end‑to‑end. Experiments show that DirtyMoCap generalizes across arbitrary marker configurations, outperforms configuration‑specific baselines in joint and vertex accuracy, and achieves up to a 100× speedup over standard PyTorch implementations, enabling the creation of a temporally coherent Kung Fu motion dataset.
By Long Wang, Shuting Zhao, Shen Yan, Siyuan Yu, Xiaoben Li, Zeyu Cai, Yumeng Hou, Yuliang Xiu