14 citations · 35 across the 5 of their papers we have counts for
13 papers · 1 filter
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution
Xinyue Wei, Haozhi Huang, Yujin Shi +3
The classic Monte Carlo path tracing can achieve high quality rendering at the cost of heavy computation. Recent works make use of deep neural networks to accelerate this process,…
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
Kunming Luo, Chuan Wang, Shuaicheng Liu +3
We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tac…
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
Kunming Luo, Chuan Wang, Nianjin Ye +2
Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove the…
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration
Nianjin Ye, Chuan Wang, Shuaicheng Liu +3
Image alignment by mesh warps, such as meshflow, is a fundamental task which has been widely applied in various vision applications(e.g., multi-frame HDR/denoising, video stabiliza…
Disentangled Image Matting
Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan +6
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.…
Content-Aware Unsupervised Deep Homography Estimation
Jirong Zhang, Chuan Wang, Shuaicheng Liu +5
Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low…