collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2026

Scene and Human in One World: Reconstruction in a Feedforward Pass

Boao Shi, Qiao Feng, Yiming Huang +1

Reconstructing humans in dynamic scenes from moving monocular cameras remains challenging due to scale ambiguity, human-scene misalignment, and occlusion interference. Rather than…

cs.CV2026

UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching

Qilin Huang, Quynh Anh Huynh, Long Le +5

Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the r…

cs.CV2025

Text2Interact: High-Fidelity and Diverse Text-to-Two-Person Interaction Generation

Qingxuan Wu, Zhiyang Dou, Chuan Guo +5

Modeling human-human interactions from text remains challenging because it requires not only realistic individual dynamics but also precise, text-consistent spatiotemporal coupling…

cs.CV2025

PhysHMR: Learning Humanoid Control Policies from Vision for Physically Plausible Human Motion Reconstruction

Qiao Feng, Yiming Huang, Yufu Wang +2

Reconstructing physically plausible human motion from monocular videos remains a challenging problem in computer vision and graphics. Existing methods primarily focus on kinematics…

cs.CV2025

PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation

Chen Wang, Chuhao Chen, Yiming Huang +4

Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limit…

cs.CV2025

Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels

Long Le, Ryan Lucas, Chen Wang +4

Inferring the physical properties of 3D scenes from visual information is a critical yet challenging task for creating interactive and realistic virtual worlds. While humans intuit…

cs.CV2025

ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking

Tingyang Zhang, Chen Wang, Zhiyang Dou +4

We propose ProTracker, a novel framework for accurate and robust long-term dense tracking of arbitrary points in videos. Previous methods relying on global cost volumes effectively…