activity
20242026
collaborators

8 papers

cs.CV2026

SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation

Anbang Wang, Yuzhuo Ao, Shangzhe Wu +1

Native 3D generative models have achieved remarkable fidelity and speed, yet they suffer from a critical limitation: inability to prescribe precise structural articulations, where…

cs.GR2026

Scene-Level Heterogeneous Physics Simulation with 3D Gaussian Splats

Xiaoyang Liu, Shangzhe Wu, Kai Han

3D Gaussian Splatting (3DGS) has achieved state-of-the-art photorealistic rendering, but the representation gap prevents these assets from being physically interactive. Production-…

cs.CV2026

Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control

Ruining Li, Yuxin Yao, Matt Zhou +5

Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but…

cs.CV2026

Articraft: An Agentic System for Scalable Articulated 3D Asset Generation

Matt Zhou, Ruining Li, Xiaoyang Lyu +6

A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close…

cs.CV2026

Particulate: Feed-Forward 3D Object Articulation

Ruining Li, Yuxin Yao, Chuanxia Zheng +4

We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion const…

cs.CV2026

Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

Shenhan Qian, Ganlin Zhang, Shangzhe Wu +1

Reconstructing and tracking dynamic 3D scenes is a fundamental challenge in computer vision. Existing methods typically decouple geometry from motion: static multi-view reconstruct…