2 citations · 4 across the 9 of their papers we have counts for
9 papers
MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
Hanwen Jiang, Zexiang Xu, Desai Xie +11
We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes -…
Expressive Gaussian Human Avatars from Monocular RGB Video
Hezhen Hu, Zhiwen Fan, Tianhao Wu +4
Nuanced expressiveness, particularly through fine-grained hand and facial expressions, is pivotal for enhancing the realism and vitality of digital human representations. In this w…
Real3D: Scaling Up Large Reconstruction Models with Real-World Images
Hanwen Jiang, Qixing Huang, Georgios Pavlakos
The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view…
CoFie: Learning Compact Neural Surface Representations with Coordinate Fields
Hanwen Jiang, Haitao Yang, Georgios Pavlakos +1
This paper introduces CoFie, a novel local geometry-aware neural surface representation. CoFie is motivated by the theoretical analysis of local SDFs with quadratic approximation.…
MultiPhys: Multi-Person Physics-aware 3D Motion Estimation
Nicolas Ugrinovic, Boxiao Pan, Georgios Pavlakos +5
We introduce MultiPhys, a method designed for recovering multi-person motion from monocular videos. Our focus lies in capturing coherent spatial placement between pairs of individu…
Learning Articulated Shape with Keypoint Pseudo-labels from Web Images
Anastasis Stathopoulos, Georgios Pavlakos, Ligong Han +1
This paper shows that it is possible to learn models for monocular 3D reconstruction of articulated objects (e.g., horses, cows, sheep), using as few as 50-150 images labeled with…