2 citations · 2 across the 8 of their papers we have counts for
11 papers
Velox: Spatial Memory Makes World Models Efficient
Qiuhong Shen, Shizun Wang, Xinchao Wang
Video world models turn actions and camera motion into long visual rollouts, making video a natural interface for interactive simulation. Such interaction requires responsive gener…
HarnessEval-W: Agentifying the Evaluation of Visual Worlds
Weiliang Chen, Haowen Sun, Jun Gao +40
A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, w…
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…
World Action Models: A Survey
Qiuhong Shen, Shihua Zhang, Yue Liao +5
World Action Models (WAMs) are embodied predictive-action models that make a forecast of the future available to action. Recent WAMs repurpose large video generation models, and a…
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Keming Wu, Zuhao Yang, Kaichen Zhang +24
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…
Make Geometry Matter for Spatial Reasoning
Shihua Zhang, Qiuhong Shen, Shizun Wang +2
Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes…
C4D: 4D Made from 3D through Dual Correspondences
Shizun Wang, Zhenxiang Jiang, Xingyi Yang +1
Recovering 4D from monocular video, which jointly estimates dynamic geometry and camera poses, is an inevitably challenging problem. While recent pointmap-based 3D reconstruction m…
Test3R: Learning to Reconstruct 3D at Test Time
Yuheng Yuan, Qiuhong Shen, Shizun Wang +2
Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherent…
PE3R: Perception-Efficient 3D Reconstruction
Jie Hu, Shizun Wang, Xinchao Wang
Recent advances in 2D-to-3D perception have enabled the recovery of 3D scene semantics from unposed images. However, prevailing methods often suffer from limited generalization, re…
GFlow: Recovering 4D World from Monocular Video
Shizun Wang, Xingyi Yang, Qiuhong Shen +2
Recovering 4D world from monocular video is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view videos, known camera parameters, or s…
MindBridge: A Cross-Subject Brain Decoding Framework
Shizun Wang, Songhua Liu, Zhenxiong Tan +1
Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currentl…