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/rakhimovv/lvt
공식 코드https://github.com/rerv/vdt
공식 코드VDT: General-purpose Video Diffusion Transformers via Mask Modeling
https://github.com/researchmm/mm-diffusion
공식 코드MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation
https://github.com/rhymes-ai/allegro
공식 코드Allegro: Open the Black Box of Commercial-Level Video Generation Model
https://github.com/rickyskywalker/dragvideo-official
공식 코드https://github.com/rishab-partha/Vid3D
공식 코드Vid3D: Synthesis of Dynamic 3D Scenes using 2D Video Diffusion
https://github.com/rocketappslab/bdmm
공식 코드Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer
https://github.com/roeiherz/AG2Video
공식 코드https://github.com/rohandkn/skribble2vid
공식 코드Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models
https://github.com/rohit901/VANE-Bench
공식 코드VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/rongpiking/customttt
공식 코드CustomTTT: Motion and Appearance Customized Video Generation via Test-Time Training
https://github.com/royorel/FFHQ-Aging-Dataset
공식 코드https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
공식 코드https://github.com/ruveydayilmaz0/bvdm
공식 코드Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields
https://github.com/ry85/Stochastic-Talking-Face-Generation-Using-Latent-Distribution-Matching
공식 코드Stochastic Talking Face Generation Using Latent Distribution Matching
https://github.com/saikatdutta/NME-VFI
공식 코드Non-linear Motion Estimation for Video Frame Interpolation using Space-time Convolutions
https://github.com/sais-fuxi/omni-video
공식 코드Omni-Video: Democratizing Unified Video Understanding and Generation
https://github.com/salesforce/lavis
공식 코드Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
https://github.com/sandai-org/magi-1
공식 코드https://github.com/sandai-org/magiattention
공식 코드https://github.com/sangyun884/rfpp
공식 코드https://github.com/sensetime-fvg/opendwm
공식 코드https://github.com/sergeytulyakov/mocogan
공식 코드MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/shalfun/DrivingDiffusion
공식 코드https://github.com/shanexiangh/aigve_tool
공식 코드AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark
https://github.com/shawnricecake/draft-attention
공식 코드DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance
https://github.com/shawnricecake/fast-car
공식 코드FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge
https://github.com/shenyi-z/duca
공식 코드Accelerating Diffusion Transformers with Dual Feature Caching
https://github.com/sherwinbahmani/3dvideogeneration
공식 코드https://github.com/shgaurav1/DVG
공식 코드https://github.com/shijianjian/ImmersePro
공식 코드ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning
https://github.com/shijianjian/stereocrafter-zero
공식 코드StereoCrafter-Zero: Zero-Shot Stereo Video Generation with Noisy Restart
https://github.com/shilin-lu/vine
공식 코드Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances
https://github.com/shim0114/ssm-meets-video-diffusion-models
공식 코드SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
https://github.com/shirgur/hp-vae-gan
공식 코드Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.