most citedPhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Long-horizon Video Generation with Slot Memory Addressed By Region Tokens

Eren Cetin, Julius Erbach, Vukasin Bozic +6

Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while still recovering what was ob…

cs.CV2026

GeoFix: Geometry Fixing for 4D Consistent Generation

Yeobin Hong, Hyojun Go, Dohun Lee +7

Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches address this through post-traini…

cs.CV2026

Level-of-Token Diffusion

Kiyohiro Nakayama, Brian Chao, Jan Ackermann +5

Image and video diffusion models allocate equal computation to every region, even when the intended scene calls for varying levels of detail. The spatial distribution of detail can…

cs.CV2026

Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers

Shuhong Zheng, Michael Oechsle, Erik Sandström +3

Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed-forward manner. Howe…

cs.CV2026

Stitched Value Model for Diffusion Alignment

Hyojun Go, Hyungjin Chung, Prune Truong +8

For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challen…

cs.GR2024★ 2 cited

PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations

Yang Zheng, Qingqing Zhao, Guandao Yang +8

Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to…