collaborators

11 papers

cs.CV2026

PURE: Refining Feed-forward 3D Reconstruction with Unlabeled Post-training

Yaolong Zhu, Peng Li, Jiahao Lu +8

Feed-forward 3D reconstruction models have achieved remarkable progress through large-scale training and strong learned priors. However, they remain fragile when applied to dynamic…

cs.CV2026

FlowHMR: Physically Plausible Motion Capture from Video

Zhanke Wang, Chengfeng Zhao, Qing Shuai +8

We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion dire…

cs.CV2026

Real2Sim in HOI: Toward Physically Plausible HOI Reconstruction from Monocular Videos

Yubo Zhao, Yujin Chai, Yunao Dong +4

Recovering 4D human-object interaction (HOI) from monocular video is a key step toward scalable 3D content creation, embodied AI, and simulation-based learning. Recent methods can…

cs.CV2026

UNIC: Neural Garment Deformation Field for Real-time Clothed Character Animation

Chengfeng Zhao, Junbo Qi, Yulou Liu +6

Simulating physically realistic garment deformations is an essential task for virtual immersive experience, which is often achieved by physics simulation methods. However, these me…

cs.CV2026

GO-Renderer: Generative Object Rendering with 3D-aware Controllable Video Diffusion Models

Zekai Gu, Shuoxuan Feng, Yansong Wang +6

Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently…

cs.CV2026

Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels

Jiahao Lu, Jiayi Xu, Wenbo Hu +5

Estimating the 3D trajectory of every pixel from a monocular video is crucial and promising for a comprehensive understanding of the 3D dynamics of videos. Recent monocular 3D trac…

cs.CV2026

UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass

Mengfei Li, Peng Li, Zheng Zhang +9

We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotat…

cs.CV2026

CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos

Chengfeng Zhao, Jiazhi Shu, Yubo Zhao +7

In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior for plausibility and consistenc…

cs.CV2025

HuPrior3R: Incorporating Human Priors for Better 3D Dynamic Reconstruction from Monocular Videos

Weitao Xiong, Zhiyuan Yuan, Jiahao Lu +3

Monocular dynamic video reconstruction faces significant challenges in dynamic human scenes due to geometric inconsistencies and resolution degradation issues. Existing methods lac…

cs.CV2025

SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction

Wenyue Chen, Peng Li, Wangguandong Zheng +6

Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and se…

cs.CV2025

Reconstructing 4D Spatial Intelligence: A Survey

Yukang Cao, Jiahao Lu, Zhisheng Huang +8

Reconstructing 4D spatial intelligence from visual observations has long been a central yet challenging task in computer vision, with broad real-world applications. These range fro…