https://github.com/ruiliu-ai/fuseformer
공식 코드FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
Papers with Code 아카이브
Video Inpainting
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The goal of Video Inpainting is to fill in missing regions of a given video sequence with contents that are both spatially and temporally coherent. Video Inpainting, also known as video completion, has many real-world applications such as undesired object removal and video restoration.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/runwayml/guided-inpainting
공식 코드https://github.com/sczhou/propainter
공식 코드ProPainter: Improving Propagation and Transformer for Video Inpainting
https://github.com/sibozhang/Depth-Guided-Inpainting
공식 코드https://github.com/subin-kim-cv/NVP
공식 코드Scalable Neural Video Representations with Learnable Positional Features
https://github.com/suhwan-cho/RGVI
공식 코드Elevating Flow-Guided Video Inpainting with Reference Generation
https://github.com/sunxm2357/TAI_video_frame_inpainting
공식 코드A Temporally-Aware Interpolation Network for Video Frame Inpainting
https://github.com/vt-vl-lab/FGVC
공식 코드https://github.com/yeates/dmt
공식 코드https://github.com/ymhzyj/UMMAFormer
공식 코드UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization
https://github.com/zhang-zx/AVID
공식 코드https://github.com/zibojia/COCOCO
공식 코드https://github.com/zrfreya/AutoRemover
공식 코드AutoRemover: Automatic Object Removal for Autonomous Driving Videos
https://github.com/89viper/Python-Deep_Video_Inpainting
https://github.com/ApolloScapeAuto/dataset-api
https://github.com/Dragoniss/minspore-phase2-E2FGVI
Towards An End-to-End Framework for Flow-Guided Video Inpainting
https://github.com/Feynman1999/MgeEditing
Learning Joint Spatial-Temporal Transformations for Video Inpainting
https://github.com/Lubsey1/inpainting_colab
https://github.com/MindSpore-scientific-2/code-5/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
INR-V: A Continuous Representation Space for Video-based Generative Tasks
https://github.com/MindSpore-scientific/code-11/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
INR-V: A Continuous Representation Space for Video-based Generative Tasks
https://github.com/TencentARC/StereoCrafter
https://github.com/hitachinsk/isvi
Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting
https://github.com/lucidrains/lumiere-pytorch
https://github.com/masterhow/fishdreamer
Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer
https://github.com/osmr/propainter
ProPainter: Improving Propagation and Transformer for Video Inpainting
https://github.com/osmr/pytorchcv
ProPainter: Improving Propagation and Transformer for Video Inpainting
https://github.com/seoungwugoh/opn-demo
https://github.com/shesay-noway/Free-Form-Video-Inpainting
Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
https://github.com/shleecs/Copy-and-Paste-Networks-for-Deep-Video-Inpainting
https://github.com/shwoo93/video_decaptioning
Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence
https://github.com/zhonghuayi/focusflow_official
Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.