Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting

24 January 2023

Papers citing "Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting"

8 / 8 papers shown

Title
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models Chaohao Xie Kai Han Kwan-Yee K. Wong VGen DiffM 114 0 0 21 Jan 2025
Transformer-based Image and Video Inpainting: Current Challenges and Future Directions Omar Elharrouss Rafat Damseh Abdelkader Nasreddine Belkacem E. Badidi Abderrahmane Lakas ViT 30 2 0 28 Jun 2024
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques Shreyank N. Gowda Yash Thakre Shashank Narayana Gowda Xiaobo Jin 24 0 0 31 Jan 2024
Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey S. Dubey Satish Kumar Singh ViT 18 32 0 17 Feb 2023
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment Kelvin C. K. Chan Shangchen Zhou Xiangyu Xu Chen Change Loy 149 388 0 27 Apr 2021
An Internal Learning Approach to Video Inpainting Haotian Zhang Long Mai N. Xu Zhaowen Wang John Collomosse Hailin Jin 48 70 0 17 Sep 2019
Image Inpainting for Irregular Holes Using Partial Convolutions Guilin Liu F. Reda Kevin J. Shih Ting-Chun Wang Andrew Tao Bryan Catanzaro 142 1,912 0 20 Apr 2018
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,471 0 17 Apr 2017