5 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…
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation
Kendong Liu, Zhiyu Zhu, Hui Liu +1
We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inf…
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Kendong Liu, Zhiyu Zhu, Chuanhao Li +3
In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improvi…