activity
20232026
most citedMoMask: Generative Masked Modeling of 3D Human Motions

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off

Shuang Liu, Ao Yu, Linkang Cheng +5

Virtual try-off (VTOFF) aims to recover canonical flat-garment representations from images of dressed persons for standardized display and downstream virtual try-on. Prior methods…

cs.CV2024

TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling

Dong Huo, Zixin Guo, Xinxin Zuo +6

Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often…

cs.CV2024★ 1 cited

GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction

Yuxuan Mu, Xinxin Zuo, Chuan Guo +7

We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D…

cs.CV2024

Generative Human Motion Stylization in Latent Space

Chuan Guo, Yuxuan Mu, Xinxin Zuo +4

Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the…

cs.CV2023★ 3 cited

MoMask: Generative Masked Modeling of 3D Human Motions

Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed +2

We introduce MoMask, a novel masked modeling framework for text-driven 3D human motion generation. In MoMask, a hierarchical quantization scheme is employed to represent human moti…