activity
20202022
most citedHiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

136 citations · 213 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2022★ 5 cited

Learning Variational Motion Prior for Video-based Motion Capture

Xin Chen, Zhuo Su, Lingbo Yang +4

Motion capture from a monocular video is fundamental and crucial for us humans to naturally experience and interact with each other in Virtual Reality (VR) and Augmented Reality (A…

cs.CV2021★ 2 cited

Thousand to One: Semantic Prior Modeling for Conceptual Coding

Jianhui Chang, Zhenghui Zhao, Lingbo Yang +3

Conceptual coding has been an emerging research topic recently, which encodes natural images into disentangled conceptual representations for compression. However, the compression…

cs.CV2020

Intrinsic Temporal Regularization for High-resolution Human Video Synthesis

Lingbo Yang, Zhanning Gao, Peiran Ren +2

Temporal consistency is crucial for extending image processing pipelines to the video domain, which is often enforced with flow-based warping error over adjacent frames. Yet for hu…

cs.CV2020★ 1 cited

Conceptual Compression via Deep Structure and Texture Synthesis

Jianhui Chang, Zhenghui Zhao, Chuanmin Jia +5

Existing compression methods typically focus on the removal of signal-level redundancies, while the potential and versatility of decomposing visual data into compact conceptual com…

cs.CV2020★ 1 cited

Implicit Subspace Prior Learning for Dual-Blind Face Restoration

Lingbo Yang, Pan Wang, Zhanning Gao +4

Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology. However, real-world image pr…

cs.CV2020★ 14 cited

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

Chaofeng Chen, Xiaoming Li, Lingbo Yang +3

Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) resu…