In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E 2 FGVI) through elaborately designed three trainable modules, namely, ﬂow completion, feature propagation, and content hallucination modules.

2022: Z. Li, Cheng Lu, Jia Qin, Chunle Guo, Mingg-Ming Cheng

Ranked #1 on Video Inpainting on YouTube-VOS 2018 val

https://arxiv.org/pdf/2204.02663v2.pdf

In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E 2 FGVI) through elaborately designed three trainable modules, namely, ﬂow completion, feature propagation, and content hallucination modules. 2022: Z. Li, Cheng Lu, Jia Qin, Chunle Guo, Mingg-Ming Cheng Ranked #1 on Video Inpainting on YouTube-VOS 2018 val https://arxiv.org/pdf/2204.02663v2.pdf

Towards An End-to-End Framework for Flow-Guided Video Inpainting

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Keeping you up to date with the latest trends and best performing architectures in this fast evolving field in computer science. Selecting papers by comparative results, citations and influence we educate you on the latest research. Consider supporting us on Patreon.com/PapersRead for feedback and ideas.

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Towards An End-to-End Framework for Flow-Guided Video Inpainting

Towards An End-to-End Framework for Flow-Guided Video Inpainting