12 papers
: 3D Reconstruction via Relative Regression
Congrong Xu, Huachen Gao, Xingyu Chen +3
Recent feed-forward geometry foundation models have demonstrated impressive generalization by recovering depth and poses in a single forward pass. However, these models are typical…
GaussiAnimate: Reconstruct and Rig Animatable Categories with Level of Dynamics
Jiaxin Wang, Dongxin Lyu, Zeyu Cai +4
Free-form bones, that conform closely to the surface, can effectively capture non-rigid deformations, but lack a kinematic structure necessary for intuitive control. Thus, we propo…
Human3R: Everyone Everywhere All at Once
Yue Chen, Xingyu Chen, Yuxuan Xue +3
We present Human3R, a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. Unlike previous approac…
TTT3R: 3D Reconstruction as Test-Time Training
Xingyu Chen, Yue Chen, Yuliang Xiu +2
Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly…
Feat2GS: Probing Visual Foundation Models with Gaussian Splatting
Yue Chen, Xingyu Chen, Anpei Chen +2
Given that visual foundation models (VFMs) are trained on extensive datasets but often limited to 2D images, a natural question arises: how well do they understand the 3D world? Wi…
ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitives
BartÅomiej Baranowski, Stefano Esposito, Patricia GschoÃmann +2
3D Gaussian Splatting (3DGS) achieves state-of-the-art image quality and real-time performance in novel view synthesis but often suffers from a suboptimal spatial distribution of p…