3 citations · 8 across the 8 of their papers we have counts for
8 papers
MegActor-: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer
Shurong Yang, Huadong Li, Juhao Wu +6
Diffusion models have demonstrated superior performance in the field of portrait animation. However, current approaches relied on either visual or audio modality to control charact…
MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
Shurong Yang, Huadong Li, Juhao Wu +5
Despite raw driving videos contain richer information on facial expressions than intermediate representations such as landmarks in the field of portrait animation, they are seldom…
Towards RGB-NIR Cross-modality Image Registration and Beyond
Huadong Li, Shichao Dong, Jin Wang +5
This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complem…
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
Huadong Li, Minhao Jing, Jiajun Liang +2
It is widely believed that sparse supervision is worse than dense supervision in the field of depth completion, but the underlying reasons for this are rarely discussed. To this en…
DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior
Shuangping Jin, Bingbing Yu, Minhao Jing +3
RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images…
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
Shichao Dong, Jin Wang, Renhe Ji +3
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by…
Explaining Deepfake Detection by Analysing Image Matching
Shichao Dong, Jin Wang, Jiajun Liang +2
This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspecti…
Deep Fusion Network for Image Completion
Xin Hong, Pengfei Xiong, Renhe Ji +1
Deep image completion usually fails to harmonically blend the restored image into existing content, especially in the boundary area. This paper handles with this problem from a new…