6 papers
CANIS: Generation-Assisted 3D Canonicalization via an Image-Semantic Bridge
Kendong Liu, Yuxin Yao, Junhui Hou
Canonicalizing 3D object orientation is fundamental to 3D understanding and analysis. Existing approaches often rely on geometric cues, although 3D canonicalization ultimately requ…
G-Skin: Learning to Bind 3D Gaussians with Generative Visual Priors
Yuxin Yao, Kendong Liu, Shiqi Zhou +2
3D Gaussian Splatting has achieved remarkable success in photorealistic and efficient rendering, leading to a rapid increase in 3D assets represented by 3D Gaussian primitives. Dir…
SkelMo: Universal Skeletal Motion Generation for 3D Rigged Shapes
Ye Tao, Yuxin Yao, Kendong Liu +2
Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fail to generalize across dive…
Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control
Ruining Li, Yuxin Yao, Matt Zhou +5
Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but…
Particulate: Feed-Forward 3D Object Articulation
Ruining Li, Yuxin Yao, Chuanxia Zheng +4
We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion const…
SmallGS: Gaussian Splatting-based Camera Pose Estimation for Small-Baseline Videos
Yuxin Yao, Yan Zhang, Zhening Huang +1
Dynamic videos with small baseline motions are ubiquitous in daily life, especially on social media. However, these videos present a challenge to existing pose estimation framework…