https://github.com/dawitmureja/ave
공식 코드The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing
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/dbstjswo505/FRAG
공식 코드https://github.com/duyguceylan/pix2video
공식 코드https://github.com/edidub/edidub-results
공식 코드https://github.com/eternalevan/fade
공식 코드FADE: Frequency-Aware Diffusion Model Factorization for Video Editing
https://github.com/facebookresearch/moviegenbench
공식 코드https://github.com/facebookresearch/videoseal
공식 코드https://github.com/fallenshock/Slicedit
공식 코드Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
https://github.com/feipengma6/vsc22-submission
공식 코드https://github.com/fire-friend/dugmatting
공식 코드https://github.com/fuankarion/audiovisual-person-search
공식 코드https://github.com/ground-a-video/ground-a-video
공식 코드Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models
https://github.com/gulucaptain/videoassembler
공식 코드MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
https://github.com/icecherylxuli/tsan
공식 코드Transcoded Video Restoration by Temporal Spatial Auxiliary Network
https://github.com/ictmcg/awesome-misinfo-video-detection
공식 코드Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
https://github.com/ictmcg/fakesv
공식 코드Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
https://github.com/isee-laboratory/referdino-plus
공식 코드ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025
https://github.com/jaehong31/raccoon
공식 코드RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives
https://github.com/junhua-liao/light-asd
공식 코드https://github.com/kepengxu/pgtformer
공식 코드Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer
https://github.com/koi953215/NaRCan
공식 코드NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
https://github.com/kuai-lab/soundini-official
공식 코드https://github.com/langmanbusi/insvie
공식 코드InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction
https://github.com/lexieyang/face_img_inpainting
공식 코드https://github.com/lichao-sun/mora
공식 코드Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
https://github.com/liliu-avril/Awesome-Segment-Anything
공식 코드https://github.com/littlespray/e-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/littlespray/ve-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/liujianzhi/echoreel
공식 코드https://github.com/lixirui142/VidToMe
공식 코드https://github.com/lutianyi0603/fuse_your_latents
공식 코드Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
https://github.com/mayuelala/magicstick
공식 코드MagicStick: Controllable Video Editing via Control Handle Transformations
https://github.com/mcg-nju/bivdiff
공식 코드https://github.com/mdswyz/TCVE
공식 코드https://github.com/mdswyz/reatco
공식 코드https://github.com/nazmul-karim170/save-tex2video
공식 코드SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.