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Chen Change Loy
Researcher at Nanyang Technological University
Publications - 111
Citations - 2881
Chen Change Loy is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 15, co-authored 111 publications receiving 782 citations. Previous affiliations of Chen Change Loy include Harbin Institute of Technology & University of Sydney.
Papers
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Focal Frequency Loss for Image Reconstruction and Synthesis.
TL;DR: In this paper, the authors propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones.
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Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
TL;DR: Adaptive pseudo augmentation (APA) as discussed by the authors employs the generator itself to augment the real data distribution with generated images, which deceives the discriminator adaptively, which can be added seamlessly to powerful contemporary GANs such as StyleGAN2.
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Playing for 3D Human Recovery
Zhongang Cai,Mingyuan Zhang,Jiawei Ren,Chen Wei,Daxuan Ren,Jiatong Li,Zhengyu Lin,Haiyu Zhao,Shuai Yi,Lei Yang,Chen Change Loy,Ziwei Liu +11 more
TL;DR: Li et al. as mentioned in this paper used video games as a training set for video-based 3D human recovery and showed that a simple frame-based baseline trained on GTA-Human outperforms more sophisticated methods by a large margin.
Patent
Image processing method and device and network training method and device
TL;DR: In this paper, the authors proposed a method for determining a guide group which is set for a target object on a to-be-processed image, enabling the guide group to comprise at least one guide point, and using the guide point to be used for indicating the position of a sampling pixel and the movement speed and direction corresponding to the sampling pixel.
Posted Content
Unsupervised 3D Shape Completion through GAN Inversion.
Junzhe Zhang,Xinyi Chen,Zhongang Cai,Liang Pan,Haiyu Zhao,Shuai Yi,Chai Kiat Yeo,Bo Dai,Chen Change Loy +8 more
TL;DR: ShapeInversion as mentioned in this paper uses a GAN pre-trained on complete shapes by searching for a latent code that gives a complete shape that best reconstructs the given partial input, which is capable of incorporating the rich prior captured in a well-trained generative model.