Papers with Code 아카이브
Video Editing
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zaplm/dc-sam
공식 코드DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency
https://github.com/zhang-tao-whu/DVIS
공식 코드https://github.com/zhang1023/fed-nerf
공식 코드FED-NeRF: Achieve High 3D Consistency and Temporal Coherence for Face Video Editing on Dynamic NeRF
https://github.com/zhaofengshi/avs-c3n
공식 코드Cross-modal Cognitive Consensus guided Audio-Visual Segmentation
https://github.com/zhijie-group/lovecon
공식 코드LOVECon: Text-driven Training-Free Long Video Editing with ControlNet
https://github.com/zhonghuayi/optical_flow_robustness_benchmark
공식 코드Benchmarking the Robustness of Optical Flow Estimation to Corruptions
https://github.com/zhongping-zhang/movie-clip
공식 코드Movie Genre Classification by Language Augmentation and Shot Sampling
https://github.com/ziwei-zheng/efficientgebd
공식 코드Rethinking the Architecture Design for Efficient Generic Event Boundary Detection
https://github.com/zyxelsa/motioncrafter
공식 코드MotionCrafter: One-Shot Motion Customization of Diffusion Models
https://github.com/zzh-tech/interpany-clearer
공식 코드Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation
https://gitlab.inria.fr/ronfard/openkinoai
공식 코드OpenKinoAI: An Open Source Framework for Intelligent Cinematography and Editing of Live Performances
https://github.com/2023-MindSpore-4/Code-5/tree/main/LEO
LEO: Generative Latent Image Animator for Human Video Synthesis
https://github.com/Blade6570/icface
ICface: Interpretable and Controllable Face Reenactment Using GANs
https://github.com/LONG-9621/body_net
https://github.com/Mind23-2/MindCode-101/tree/main/LEO
LEO: Generative Latent Image Animator for Human Video Synthesis
https://github.com/Mind23-2/MindCode-3/tree/main/LEO
LEO: Generative Latent Image Animator for Human Video Synthesis
https://github.com/ShaoTengLiu/Video-P2P
https://github.com/SilvioGiancola/SoccerNetv2-DevKit
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/adymaharana/adapt-inf
RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives
https://github.com/aimagelab/rmsnet_soccer
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/ali-vilab/vace
https://github.com/charlescheng0117/p2pvg
https://github.com/code-implementation1/Code4/tree/main/LEO
LEO: Generative Latent Image Animator for Human Video Synthesis
https://github.com/codepassionor/T2I_Adapter
Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion
https://github.com/facebookresearch/nonrigid_nerf
https://github.com/frankhome61/nonrigid-nerf
https://github.com/gulvarol/bodynet
https://github.com/haoyuhsu/autovfx
AutoVFX: Physically Realistic Video Editing from Natural Language Instructions
https://github.com/huochaitiantang/pytorch-fast-matting-portrait
https://github.com/levyisthebest/echopulse_prelease
Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
https://github.com/ljl5261/mmm-rs
https://github.com/lucidrains/lumiere-pytorch
https://github.com/mindspore-lab/mindone/tree/master/examples/moviegen
https://github.com/obvious-research/OnlyFlow
OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion Models
https://github.com/omerbt/tokenflow
TokenFlow: Consistent Diffusion Features for Consistent Video Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.