3 citations · 10 across the 14 of their papers we have counts for
6 papers · 1 filter
DiffusionPhase: Motion Diffusion in Frequency Domain
Weilin Wan, Yiming Huang, Shutong Wu +4
In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques…
Disentangled Clothed Avatar Generation from Text Descriptions
Jionghao Wang, Yuan Liu, Zhiyang Dou +7
In this paper, we introduce a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated ava…
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation
Wenyang Zhou, Zhiyang Dou, Zeyu Cao +7
We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation. Current state-of-the-art generative diffusion models have produced impressiv…
StructRe: Rewriting for Structured Shape Modeling
Jiepeng Wang, Hao Pan, Yang Liu +3
Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structure…
TLControl: Trajectory and Language Control for Human Motion Synthesis
Weilin Wan, Zhiyang Dou, Taku Komura +3
Controllable human motion synthesis is essential for applications in AR/VR, gaming and embodied AI. Existing methods often focus solely on either language or full trajectory contro…
Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models
Zhengming Yu, Zhiyang Dou, Xiaoxiao Long +9
We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation wit…