https://github.com/llyx97/fetv
공식 코드FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Papers with Code 아카이브
Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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<span style="color:grey; opacity: 0.6">( Various Video Generation Tasks. Gif credit: MaGViT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lpengyang/motionclone
공식 코드MotionClone: Training-Free Motion Cloning for Controllable Video Generation
https://github.com/lzzzzzm/ii-world
공식 코드$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
https://github.com/magref-video/magref
공식 코드https://github.com/maple-aigc/inflvg
공식 코드InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO
https://github.com/mar-cry/ophora
공식 코드Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model
https://github.com/maxin-cn/Latte
공식 코드https://github.com/mayuelala/followyourclick
공식 코드Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts
https://github.com/mayuelala/followyourpose
공식 코드Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
https://github.com/mayuelala/magicstick
공식 코드MagicStick: Controllable Video Editing via Control Handle Transformations
https://github.com/mcg-nju/bivdiff
공식 코드https://github.com/meigen-ai/multitalk
공식 코드Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation
https://github.com/merlresearch/TI2V-Zero
공식 코드TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
https://github.com/metro-smiles/ProgGen
공식 코드https://github.com/mezzelfo/MotionCraft
공식 코드https://github.com/mfurkanilaslan/vg-tvp
공식 코드VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
https://github.com/michi-3000/fundus2video
공식 코드https://github.com/microsoft/nuwa
공식 코드NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
https://github.com/microsoft/peekaboo
공식 코드https://github.com/microsoft/reducio-vae
공식 코드https://github.com/microsoft/vidtok
공식 코드https://github.com/midas-research/linguistically-informed-frame-interpolation
공식 코드https://github.com/mingxiangl/devil
공식 코드Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
https://github.com/minnie-lin/awesome-physics-cognition-based-video-generation
공식 코드Exploring the Evolution of Physics Cognition in Video Generation: A Survey
https://github.com/mit-han-lab/x-attention
공식 코드XAttention: Block Sparse Attention with Antidiagonal Scoring
https://github.com/mlvlab/DDMI
공식 코드https://github.com/modelscope/DiffSynth-Studio
공식 코드ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning
https://github.com/modelscope/modelscope
공식 코드VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
https://github.com/musikisomorphie/swd
공식 코드https://github.com/myniuuu/mofa-video
공식 코드https://github.com/nadeemlab/CEP
공식 코드https://github.com/nazmul-karim170/Free-Editor
공식 코드https://github.com/ndrwmlnk/awesome-video-diffusion-models
공식 코드https://github.com/nihaomiao/cvpr23_lfdm
공식 코드Conditional Image-to-Video Generation with Latent Flow Diffusion Models
https://github.com/nivha/single_video_generation
공식 코드https://github.com/nus-hpc-ai-lab/opendit
공식 코드DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.