https://github.com/taldatech/ddlp
공식 코드DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
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/tang1705/seeclear-neurips24
공식 코드SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
https://github.com/tencent/hunyuanvideo
공식 코드HunyuanVideo: A Systematic Framework For Large Video Generative Models
https://github.com/tencentarc/ditctrl
공식 코드https://github.com/tencentarc/divot
공식 코드Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation
https://github.com/tensorflow/tensor2tensor
공식 코드VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
https://github.com/thisisbillhe/nar
공식 코드Neighboring Autoregressive Modeling for Efficient Visual Generation
https://github.com/thu-coai/vpo
공식 코드VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
https://github.com/thu-ml/SageAttention
공식 코드SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
https://github.com/thu-ml/spargeattn
공식 코드SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference
https://github.com/thu-nics/VGDFR
공식 코드VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate
https://github.com/thu-nics/dlfr-vae
공식 코드DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation
https://github.com/thudm/cogvideo
공식 코드CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
https://github.com/thudm/visionreward
공식 코드https://github.com/thuhcsi/s2g-mddiffusion
공식 코드Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
https://github.com/thunil/TecoGAN
공식 코드Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/thunlp/acdit
공식 코드ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
https://github.com/tianxingwu/freeinit
공식 코드FreeInit: Bridging Initialization Gap in Video Diffusion Models
https://github.com/tiger-ai-lab/anyv2v
공식 코드AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
https://github.com/tmelyralab/musetalk
공식 코드MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling
https://github.com/transpchan/Live3D
공식 코드https://github.com/tsujuifu/pytorch_tvc
공식 코드Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation
https://github.com/ttthhl/protecting_your_video_content
공식 코드Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations
https://github.com/universome/stylegan-v
공식 코드StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
https://github.com/vchitect/vbench
공식 코드VBench: Comprehensive Benchmark Suite for Video Generative Models
https://github.com/vidcapbench/vidcapbench
공식 코드VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation
https://github.com/video-as-agent/videoagent
공식 코드https://github.com/videocrafter/animate-a-story
공식 코드Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
https://github.com/vincentqqu/e2hqv
공식 코드E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
https://github.com/vision-x-nyu/pisa-experiments
공식 코드PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop
https://github.com/vivoCameraResearch/Hyper-Motion
공식 코드HyperMotion: DiT-Based Pose-Guided Human Image Animation of Complex Motions
https://github.com/voletiv/mcvd-pytorch
공식 코드MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
https://github.com/vpx-ecnu/find-website
공식 코드FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models
https://github.com/vqassessment/dover
공식 코드https://github.com/vvictoryuki/animatezero
공식 코드AnimateZero: Video Diffusion Models are Zero-Shot Image Animators
https://github.com/wan-video/wan2.1
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.