3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models
Yiming Hao, Mutian Xu, Chongjie Ye +4
Personalizing visual generative models to meet specific user needs has gained increasing attention, yet current methods like Low-Rank Adaptation (LoRA) remain impractical due to th…
UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents
Xufan He, Yushuang Wu, Xiaoyang Guo +5
Part-level 3D generation is essential for applications requiring decomposable and structured 3D synthesis. However, existing methods either rely on implicit part segmentation with…
ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation
Jiahao Chang, Chongjie Ye, Yushuang Wu +6
Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield sever…
Stable-Sim2Real: Exploring Simulation of Real-Captured 3D Data with Two-Stage Depth Diffusion
Mutian Xu, Chongjie Ye, Haolin Liu +3
3D data simulation aims to bridge the gap between simulated and real-captured 3D data, which is a fundamental problem for real-world 3D visual tasks. Most 3D data simulation method…
Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging
Chongjie Ye, Yushuang Wu, Ziteng Lu +5
With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due…