17 papers
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…
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
Stanislaw Szymanowicz, Minghao Chen, Jianyuan Wang +2
Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive bi…
VGGT-
Jianyuan Wang, Minghao Chen, Shangzhan Zhang +7
Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing geometry-aware features use…
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…
Probing into Camera Control of Video Models
Chen Hou, Christian Rupprecht
Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometrically meaningful content. Ex…
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…