https://github.com/JianZhang-chick/EMOdiffhead
공식 코드EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion
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/JiangkaiWu/Promptus
공식 코드Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion
https://github.com/KaiyueSun98/T2V-CompBench
공식 코드T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
https://github.com/KwaiVGI/I2V-Adapter
공식 코드I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
https://github.com/KwaiVGI/LivePortrait
공식 코드LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
https://github.com/Lightricks/LTX-Video
공식 코드https://github.com/MKFMIKU/VIDM
공식 코드https://github.com/Manu21JC/Dormant
공식 코드Dormant: Defending against Pose-driven Human Image Animation
https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video
공식 코드https://github.com/NUS-HPC-AI-Lab/VideoSys
공식 코드https://github.com/PJLab-ADG/DriveArena
공식 코드DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes
https://github.com/PKU-YuanGroup/ConsisID
공식 코드Identity-Preserving Text-to-Video Generation by Frequency Decomposition
https://github.com/PKU-YuanGroup/OpenS2V-Nexus
공식 코드OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
https://github.com/PaddlePaddle/PaddleMIX
공식 코드https://github.com/PallottaEnrico/SyncVP
공식 코드SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction
https://github.com/PingYu-iris/SA-GCN
공식 코드https://github.com/PolymathicAI/lola
공식 코드Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
https://github.com/QualityAssessment/DOVER
공식 코드https://github.com/QuanjianSong/LightMotion
공식 코드A Light and Tuning-free Method for Simulating Camera Motion in Video Generation
https://github.com/RAISELab-atUVA/Projected-Diffusion
공식 코드https://github.com/RQ-Wu/LAMP
공식 코드LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
https://github.com/Rbrq03/ClassDiffusion
공식 코드ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
https://github.com/RedShift51/fast-latent-decoders
공식 코드Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation
https://github.com/Shenyi-Z/ToCa
공식 코드Accelerating Diffusion Transformers with Token-wise Feature Caching
https://github.com/Singularity42/Sync-DRAW
공식 코드Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
https://github.com/Singularity42/cap2vid
공식 코드https://github.com/SobeyMIL/MVOC
공식 코드MVOC: a training-free multiple video object composition method with diffusion models
https://github.com/TIGER-AI-Lab/ConsistI2V
공식 코드ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
https://github.com/Tencent/DepthCrafter
공식 코드DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
https://github.com/TencentARC/MotionCtrl
공식 코드MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
https://github.com/Tian-one/FCVG
공식 코드Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
https://github.com/Tobi-r9/RaMViD
공식 코드https://github.com/UTAustin-SwarmLab/NeuS-V
공식 코드Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification
https://github.com/Ugness/MeBT
공식 코드https://github.com/VAST-AI-Research/TripoSG
공식 코드TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
https://github.com/Vchitect/Evaluation-Agent
공식 코드Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.