collaborators

12 papers

cs.CV2026

ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

Yukang Cao, Haozhe Xie, Beichen Wen +13

The paper presents ACE, a data collection system that records synchronized multimodal streams—including egocentric and multi-view video, full-body and hand motion, object geometry,…

cs.CV2026

Collaborative Multi-Modal Coding for High-Quality 3D Generation

Ziang Cao, Zhaoxi Chen, Liang Pan +1

3D content inherently encompasses multi-modal characteristics and can be projected into different modalities (e.g., RGB images, RGBD, and point clouds). Each modality exhibits dist…

cs.CV2026

PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects

Ziang Cao, Yinghao Liu, Haitian Li +5

Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods eithe…

cs.CV2026

Is Your Driving World Model an All-Around Player?

Lingdong Kong, Ao Liang, Tianyi Yan +20

Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…

cs.CV2026

Scaling Spatial Intelligence with Multimodal Foundation Models

Zhongang Cai, Ruisi Wang, Chenyang Gu +26

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation m…

cs.CV2026

HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions

Yukang Cao, Haozhe Xie, Fangzhou Hong +4

We present HSImul3R, a unified framework for simulation-ready 3D reconstruction of human-scene interactions (HSI) from casual captures, including sparse-view images and monocular v…