4 citations · 4 across the 7 of their papers we have counts for
9 papers
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery
Zhikang Xu, Jiarui Xing, Jian Wang
We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridges statistical robustness and…
RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration
Jiahao Luo, Hao Zhang, Jianqi Chen +9
We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide…
Uncertainty in a Single Pass: A Closed-Form Identity for One-Step Flow Matching
Jiarui Xing, Song Wang, Jian Wang
Flow matching provides a highly effective framework for generative modeling, yet estimating the uncertainty of its generated samples remains a fundamental challenge. Existing metho…
MGPC: Multimodal Network for Generalizable Point Cloud Completion With Modality Dropout and Progressive Decoding
Jiangyuan Liu, Yuhao Zhao, Hongxuan Ma +3
Point cloud completion aims to recover complete 3D geometry from partial observations caused by limited viewpoints and occlusions. Existing learning-based works, including 3D Convo…
RigMo: Unifying Rig and Motion Learning for Generative Animation
Hao Zhang, Jiahao Luo, Bohui Wan +7
Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipeline…
T2Bs: Text-to-Character Blendshapes via Video Generation
Jiahao Luo, Chaoyang Wang, Michael Vasilkovsky +8
We present T2Bs, a framework for generating high-quality, animatable character head morphable models from text by combining static text-to-3D generation with video diffusion. Text-…
SEGA: Drivable 3D Gaussian Head Avatar from a Single Image
Chen Guo, Zhuo Su, Liao Wang +8
Creating photorealistic 3D head avatars from limited input has become increasingly important for applications in virtual reality, telepresence, and digital entertainment. While rec…
ControlNeXt: Powerful and Efficient Control for Image and Video Generation
Bohao Peng, Jian Wang, Yuechen Zhang +3
Diffusion models have demonstrated remarkable and robust abilities in both image and video generation. To achieve greater control over generated results, researchers introduce addi…
Matting by Generation
Zhixiang Wang, Baiang Li, Jian Wang +4
This paper introduces an innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge. Our method harnesses the cap…