Papers with Code 아카이브
Video Editing
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Adamdad/vico
공식 코드https://github.com/Carlyx/DPE
공식 코드DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
https://github.com/ChenHsing/Awesome-Video-Diffusion-Models
공식 코드https://github.com/Chenfeng1271/SVDiff
공식 코드Streaming Video Diffusion: Online Video Editing with Diffusion Models
https://github.com/ChenyangLEI/deep-video-prior
공식 코드https://github.com/Eyeline-Research/FlashDepth
공식 코드FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
https://github.com/Francis-Rings/MotionEditor
공식 코드MotionEditor: Editing Video Motion via Content-Aware Diffusion
https://github.com/Francis-Rings/MotionFollower
공식 코드MotionFollower: Editing Video Motion via Lightweight Score-Guided Diffusion
https://github.com/HyeonHo99/Video-Motion-Customization
공식 코드VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/JonathonLuiten/Dynamic3DGaussians
공식 코드Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis
https://github.com/MaxReimann/video-stream-consistency
공식 코드Interactive Control over Temporal Consistency while Stylizing Video Streams
https://github.com/PardoAlejo/LearningToCut
공식 코드https://github.com/QuanjianSong/UniVST
공식 코드UniVST: A Unified Framework for Training-free Localized Video Style Transfer
https://github.com/RQ-Wu/LAMP
공식 코드LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
https://github.com/STEM-Inv/stem-inv
공식 코드https://github.com/SobeyMIL/MVOC
공식 코드MVOC: a training-free multiple video object composition method with diffusion models
https://github.com/TencentARC/VideoPainter
공식 코드VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
https://github.com/acceleration-magnification/sources
공식 코드https://github.com/acherstyx/autotransition
공식 코드AutoTransition: Learning to Recommend Video Transition Effects
https://github.com/afruehstueck/VIVE3D
공식 코드VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs
https://github.com/alibaba/effived
공식 코드EffiVED:Efficient Video Editing via Text-instruction Diffusion Models
https://github.com/alonzoleeeooo/stablev2v
공식 코드StableV2V: Stablizing Shape Consistency in Video-to-Video Editing
https://github.com/amazon-science/instruct-video-to-video
공식 코드https://github.com/amirziai/kissing-detector
공식 코드https://github.com/amjltc295/Free-Form-Video-Inpainting
공식 코드Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
https://github.com/baaivision/vid2vid-zero
공식 코드Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models
https://github.com/baidu-research/vidpress-sports
공식 코드https://github.com/billyxyb/facednerf
공식 코드FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
https://github.com/billyxyb/fdnerf
공식 코드FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
https://github.com/chenyanglei/all-in-one-deflicker
공식 코드Blind Video Deflickering by Neural Filtering with a Flawed Atlas
https://github.com/chenyangqiqi/fatezero
공식 코드FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
https://github.com/cplusx/rich_context_l2i
공식 코드Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation
https://github.com/custommetrics-sst/sst_customevaluationmetrics
공식 코드SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing
https://github.com/cvlab-kaist/Chrono
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.