92 citations · 594 across the 74 of their papers we have counts for
8 papers · 2 filters
Focal Frequency Loss for Image Reconstruction and Synthesis
Liming Jiang, Bo Dai, Wayne Wu +1
Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and gener…
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs
Xingang Pan, Bo Dai, Ziwei Liu +2
Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image man…
Unsupervised Landmark Learning from Unpaired Data
Yinghao Xu, Ceyuan Yang, Ziwei Liu +2
Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses. These methods learn landmarks by encourag…
Video Representation Learning with Visual Tempo Consistency
Ceyuan Yang, Yinghao Xu, Bo Dai +1
Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition. In this work, we demonstrate that visual tempo can also serve as a…
Intra- and Inter-Action Understanding via Temporal Action Parsing
Dian Shao, Yue Zhao, Bo Dai +1
Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrat…
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
Dian Shao, Yue Zhao, Bo Dai +1
On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capa…