1 citations · 1 across the 4 of their papers we have counts for
5 papers
FullPart: Generating each 3D Part at Full Resolution
Lihe Ding, Shaocong Dong, Yaokun Li +10
Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficie…
MoDiT: Learning Highly Consistent 3D Motion Coefficients with Diffusion Transformer for Talking Head Generation
Yucheng Wang, Dan Xu
Audio-driven talking head generation is critical for applications such as virtual assistants, video games, and films, where natural lip movements are essential. Despite progress in…
From One to More: Contextual Part Latents for 3D Generation
Shaocong Dong, Lihe Ding, Xiao Chen +10
Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth da…
Interactive3D: Create What You Want by Interactive 3D Generation
Shaocong Dong, Lihe Ding, Zhanpeng Huang +3
3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do no…
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Lihe Ding, Shaocong Dong, Zhanpeng Huang +5
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-…