activity
20182022
most citedStyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation

50 citations · 74 across the 9 of their papers we have counts for

collaborators

17 papers

cs.CV202150 cited

StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation

Wonjong Jang, Gwangjin Ju, Yucheol Jung +3

We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically creates a realistic and detaile…

cs.CV2021

High-Resolution Optical Flow from 1D Attention and Correlation

Haofei Xu, Jiaolong Yang, Jianfei Cai +2

Optical flow is inherently a 2D search problem, and thus the computational complexity grows quadratically with respect to the search window, making large displacements matching inf…

cs.GR2021

Learning and Exploring Motor Skills with Spacetime Bounds

Li-Ke Ma, Zeshi Yang, Xin Tong +2

Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables…

cs.CV20206 cited

Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence

Yu Deng, Jiaolong Yang, Xin Tong

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is repr…

cs.CV20202 cited

Object-based Illumination Estimation with Rendering-aware Neural Networks

Xin Wei, Guojun Chen, Yue Dong +2

We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. Conventional inverse rendering is too computat…

cs.CV20204 cited

Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion

Peng-Shuai Wang, Yang Liu, Xin Tong

Acquiring complete and clean 3D shape and scene data is challenging due to geometric occlusion and insufficient views during 3D capturing. We present a simple yet effective deep le…