Papers with Code 아카이브
Video Generation
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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/wangjiarui153/AIGV-Assessor
공식 코드https://github.com/wangwenhao0716/benchufo
공식 코드VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation
https://github.com/wangwenhao0716/vidprom
공식 코드VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models
https://github.com/weifeng-chen/control-a-video
공식 코드https://github.com/weixi-feng/tc-bench
공식 코드TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
https://github.com/wengzejia1/genrec
공식 코드GenRec: Unifying Video Generation and Recognition with Diffusion Models
https://github.com/wenhao728/vorta
공식 코드VORTA: Efficient Video Diffusion via Routing Sparse Attention
https://github.com/wentaol86/awesome-human-body-video-generation
공식 코드A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights
https://github.com/weslee88524/c-drag-official-repo
공식 코드C-Drag: Chain-of-Thought Driven Motion Controller for Video Generation
https://github.com/westlake-repl/leanvae
공식 코드LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models
https://github.com/wileewang/TransPixar
공식 코드TransPixeler: Advancing Text-to-Video Generation with Transparency
https://github.com/willi-menapace/PlayableEnvironments
공식 코드https://github.com/willi-menapace/PlayableVideoGeneration
공식 코드https://github.com/wilson1yan/VideoGPT
공식 코드https://github.com/wilson1yan/povt
공식 코드Patch-based Object-centric Transformers for Efficient Video Generation
https://github.com/wilson1yan/teco
공식 코드https://github.com/wisdomdeng/AdaptiveRendering
공식 코드https://github.com/wutao-cs/customcrafter
공식 코드CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities
https://github.com/wuwuwuyue/decof
공식 코드https://github.com/wuyushuwys/FMEDiffusion
공식 코드Fast and Memory-Efficient Video Diffusion Using Streamlined Inference
https://github.com/wyhsirius/g3an-project
공식 코드G3AN: Disentangling Appearance and Motion for Video Generation
https://github.com/wzzheng/doe
공식 코드Doe-1: Closed-Loop Autonomous Driving with Large World Model
https://github.com/wzzheng/stag
공식 코드Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model
https://github.com/x-plug/mm_storyagent
공식 코드https://github.com/xiaomi-mlab/mila.github.io
공식 코드MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving
https://github.com/xiaoqian-shen/mostgan-v
공식 코드https://github.com/xinyiying/rfr
공식 코드https://github.com/xrenaa/Music-Dance-Video-Synthesis
공식 코드Music-oriented Dance Video Synthesis with Pose Perceptual Loss
https://github.com/xsimba123/demos-of-csf-sa
공식 코드Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation
https://github.com/yanghb22-fdu/hi3d-official
공식 코드Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
https://github.com/yangling0818/contextdiff
공식 코드Contextualized Diffusion Models for Text-Guided Image and Video Generation
https://github.com/yangling0818/trans4d
공식 코드Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
https://github.com/yangling0818/videotetris
공식 코드https://github.com/yangzhaojason/DualDiff
공식 코드DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance
https://github.com/yanivnik/sinfusion-code
공식 코드SinFusion: Training Diffusion Models on a Single Image or Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.