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/18445864529/mavin
공식 코드MAVIN: Multi-Action Video Generation with Diffusion Models via Transition Video Infilling
https://github.com/360cvgroup/qihoo-t2x
공식 코드Qihoo-T2X: An Efficient Proxy-Tokenized Diffusion Transformer for Text-to-Any-Task
https://github.com/983632847/sam-for-videos
공식 코드https://github.com/AIGeeksGroup/PresentAgent
공식 코드PresentAgent: Multimodal Agent for Presentation Video Generation
https://github.com/AILab-CVC/FreeNoise
공식 코드FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
https://github.com/Adam-Haile/vita-research-group
공식 코드Synthesizing Audio from Silent Video using Sequence to Sequence Modeling
https://github.com/Adamdad/vico
공식 코드https://github.com/Ali-Stanford/MisleadingGalleryOfFluidMotionByAI
공식 코드A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence
https://github.com/AliaksandrSiarohin/monkey-net
공식 코드https://github.com/Bei-Jin/STMFANet
공식 코드https://github.com/CSfufu/VidSketch
공식 코드VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control
https://github.com/CandleLabAI/MotionAura-ICLR-2025
공식 코드MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
https://github.com/CelebV-Text/CelebV-Text
공식 코드https://github.com/ChenHsing/Awesome-Video-Diffusion-Models
공식 코드https://github.com/ChenyangSi/FreeU
공식 코드https://github.com/ChristophReich1996/Multi-StyleGAN
공식 코드Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
https://github.com/Chuge0335/EDG
공식 코드https://github.com/Droliven/SViMo_code
공식 코드SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios
https://github.com/DuNGEOnmassster/VideoGen-of-Thought
공식 코드VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation
https://github.com/EnVision-Research/MotionDreamer
공식 코드Motion Dreamer: Realizing Physically Coherent Video Generation through Scene-Aware Motion Reasoning
https://github.com/EvalCrafter/EvalCrafter
공식 코드EvalCrafter: Benchmarking and Evaluating Large Video Generation Models
https://github.com/FeiiYin/StyleHEAT
공식 코드StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
https://github.com/Francis-Rings/MotionFollower
공식 코드MotionFollower: Editing Video Motion via Lightweight Score-Guided Diffusion
https://github.com/Francis-Rings/StableAnimator
공식 코드StableAnimator: High-Quality Identity-Preserving Human Image Animation
https://github.com/FudanVI/compositional-scene-representation-toolbox/tree/main/video-decomposition-prediction
공식 코드https://github.com/FuxiVirtualHuman/Write-a-Speaker
공식 코드Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation
https://github.com/G-U-N/Phased-Consistency-Model
공식 코드https://github.com/GongyeLiu/StyleCrafter
공식 코드StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
https://github.com/Guohanzhong/OSA-LCM
공식 코드Real-time One-Step Diffusion-based Expressive Portrait Videos Generation
https://github.com/HReynaud/EchoDiffusion
공식 코드Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
https://github.com/HelloVision/HelloMeme
공식 코드https://github.com/HyeonHo99/Video-Motion-Customization
공식 코드VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/Jeckinchen/PhyCoBench
공식 코드https://github.com/Ji4chenLi/t2v-turbo
공식 코드T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.