arXiv Computer Vision
Sep 15

MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons

arXiv:2604.28130v4 Announce Type: replace Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts join...

By Kehong Gong, Zhengyu Wen, Dao Thien Phong, Mingxi Xu, Weixia He, Qi Wang, Ning Zhang, Zhengyu Li, Guanli Hou, Dongze Lian, Xiaoyu He, Mingyuan Zhang, Hanwang Zhang
arXiv Computer Vision
Sep 23

HARMONY: Hierarchical Agentic Reasoning for MONocular Image-to-Scene Synthesis

HARMONY is a hierarchical chain-of-thought framework that reconstructs complete 3D indoor scenes from a single monocular image. It combines agentic reasoning with visual geometry foundation models, starting with camera calibration and semantic layout recovery, then placing objects hierarchically while refining geometry with point cloud estimations. The method achieves semantically consistent scenes that align perceptually with the input image, outperforming existing baselines on synthetic and real-world data.

By Shufan Sun, Chen Wang, Enxin Song, Jiatao Gu, Lingjie Liu
arXiv Computer Vision
Sep 18

SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes

SceneTeract is a verification interface that separates semantic action understanding from physical feasibility in indoor 3D scenes. It decomposes activities into atomic actions and performs explicit geometric checks to determine executability, providing diagnostic traces for failures. The system reveals widespread functional and accessibility issues in synthetic scenes, shows that existing VLMs over‑predict action feasibility, and improves VLM performance through post‑training with verifier feedback, with benefits that generalize to real‑world scenes.

By L\'eopold Maillard, Francis Engelmann, Tom Durand, Boxiao Pan, Yang You, Leonidas Guibas, Maks Ovsjanikov