1 citations · 3 across the 25 of their papers we have counts for
7 papers · 2 filters
LLM2R: From Large Language Models to 3D Reassemblers
Zhouhan Zhong, Qiuhong Shen, Xingyi Yang +2
3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because the placement of a part is r…
Geometric and Semantic Coupling for Interaction Understanding in 3D Scenes
Hanyang Kong, Xingyi Yang
Understanding interaction in a 3D scene requires recovering movable parts, their motion, and where they can be operated. These quantities are related, and their predictions can inf…
Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction
Tianbo Pan, Xingyi Yang, Shizun Wang +1
Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption: the scenes is entire distractor-free with perfect c…
BadWorld: Adversarial Attacks on World Models
Linghui Shen, Mingyue Cui, Xingyi Yang
Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to adve…
Q-ARVD: Quantizing Autoregressive Video Diffusion Models
Siao Tang, Xinyin Ma, Gongfan Fang +2
Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time interactive video generation and…
ReactiveGWM: Flexible Control and NPC Reactivity in Game World Models
Zeqing Wang, Danze Chen, Zhaohu Xing +4
Existing game world models typically adopt role-specific interactions, where player and NPC roles are bound to fixed characters. This limits their flexibility in multi-character ga…
Free Geometry: Refining 3D Reconstruction from Longer Versions of Itself
Yuhang Dai, Xingyi Yang
Feed-forward 3D reconstruction models are efficient but rigid: once trained, they perform inference in a zero-shot manner and cannot adapt to the test scene. As a result, visually…