activity
20202025
most citedDeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

65 citations · 172 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CV2025

MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention

Yuhan Wang, Fangzhou Hong, Shuai Yang +3

Multiview diffusion models have shown considerable success in image-to-3D generation for general objects. However, when applied to human data, existing methods have yet to deliver…

cs.CV2025

Balanced Image Stylization with Style Matching Score

Yuxin Jiang, Liming Jiang, Shuai Yang +3

We present Style Matching Score (SMS), a novel optimization method for image stylization with diffusion models. Balancing effective style transfer with content preservation is a lo…

cs.CV2023

PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation

Jianhui Yu, Hao Zhu, Liming Jiang +3

Recent advances in zero-shot text-to-3D human generation, which employ the human model prior (eg, SMPL) or Score Distillation Sampling (SDS) with pre-trained text-to-image diffusio…

cs.CV2023

StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation

Yuhan Wang, Liming Jiang, Chen Change Loy

Unconditional video generation is a challenging task that involves synthesizing high-quality videos that are both coherent and of extended duration. To address this challenge, rese…

cs.CV2023★ 1 cited

Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation

Yuxin Jiang, Liming Jiang, Shuai Yang +1

Automatic high-quality rendering of anime scenes from complex real-world images is of significant practical value. The challenges of this task lie in the complexity of the scenes,…

cs.CV2023

GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation

Shuai Yang, Liming Jiang, Ziwei Liu +1

Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired…