https://github.com/Vchitect/LaVie
공식 코드LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
Papers with Code 아카이브
Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/XuweiyiChen/Pix2Gif
공식 코드https://github.com/XuweiyiChen/UniCtrl
공식 코드https://github.com/Y-ichen/FlexiFilm
공식 코드https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy
공식 코드Diffusion Models: A Comprehensive Survey of Methods and Applications
https://github.com/Zasder3/Latent-Neural-Differential-Equations-for-Video-Generation
공식 코드https://github.com/Zehong-Ma/ComfyUI-MagCache
공식 코드https://github.com/ZqlwMatt/Tex4D
공식 코드Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models
https://github.com/a-suozhang/vidit-q
공식 코드https://github.com/aHapBean/VideoREPA
공식 코드VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models
https://github.com/ai-forever/kandinsky-3
공식 코드https://github.com/ai-forever/kandinskyvideo
공식 코드FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline
https://github.com/aigc-apps/easyanimate
공식 코드EasyAnimate: A High-Performance Long Video Generation Method based on Transformer Architecture
https://github.com/ailab-cvc/videocrafter
공식 코드VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
https://github.com/ailab-cvc/videogen-eval
공식 코드VideoGen-Eval: Agent-based System for Video Generation Evaluation
https://github.com/alexlee-gk/video_prediction
공식 코드https://github.com/ali-vilab/VGen
공식 코드DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
https://github.com/ali-vilab/cdt
공식 코드Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
https://github.com/ali-vilab/unianimate-dit
공식 코드https://github.com/alibaba-damo-academy/DyDiT
공식 코드DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation
https://github.com/alibaba/Tora
공식 코드Tora: Trajectory-oriented Diffusion Transformer for Video Generation
https://github.com/alibaba/animate-anything
공식 코드AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
https://github.com/amd-aig-aima/amd-hummingbird-t2v
공식 코드https://github.com/andrewjywang/SEENet
공식 코드Order Matters: Shuffling Sequence Generation for Video Prediction
https://github.com/andyplus1/conmo
공식 코드ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer
https://github.com/anonymous202203/moso
공식 코드MOSO: Decomposing MOtion, Scene and Object for Video Prediction
https://github.com/answers111/opencarboneval
공식 코드OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models
https://github.com/antgroup/echomimic_v2
공식 코드EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation
https://github.com/araachie/yoda
공식 코드Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video Generation
https://github.com/arthur-qiu/FreeNoise-AnimateDiff
공식 코드FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
https://github.com/arthur-qiu/freenoise-lavie
공식 코드FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
https://github.com/arthur-qiu/freetraj
공식 코드FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models
https://github.com/arthur-qiu/stylefacev
공식 코드StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3
https://github.com/astra-vision/SceneRF
공식 코드SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
https://github.com/aviclu/long-context-qa-contrast
공식 코드Long Context Question Answering via Supervised Contrastive Learning
https://github.com/aviclu/seq-contrast-qa
공식 코드Long Context Question Answering via Supervised Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.