Awesome-Video-Diffusion and Awesome-Video-Diffusion-Models
These are near-duplicate curated lists with overlapping scope—one is a community-maintained collection of video diffusion resources while the other is a survey paper's accompanying repository—making them competitive alternatives rather than complementary tools.
About Awesome-Video-Diffusion
showlab/Awesome-Video-Diffusion
A curated list of recent diffusion models for video generation, editing, and various other applications.
Organized into 20+ specialized categories, the collection spans foundation models and inference frameworks (HunyuanVideo, LTX-Video, Cosmos) alongside task-specific implementations for controllable generation, motion customization, video enhancement, talking head synthesis, and emerging domains like 4D content and game generation. The curated entries link to implementations built on diffusion architectures with complementary techniques including flow matching, reinforcement learning policies, and 3D/NeRF priors for physics-aware synthesis. Each resource includes direct GitHub repositories, arXiv papers, and project websites for reproducibility and comparative benchmarking across the video diffusion ecosystem.
About Awesome-Video-Diffusion-Models
ChenHsing/Awesome-Video-Diffusion-Models
[CSUR] A Survey on Video Diffusion Models
Comprehensive curated resource covering diffusion-based approaches for video generation and editing tasks, with categorized sections spanning text-to-video synthesis, pose/instruction/sound-guided generation, video completion, and editing methods. Organizes research across training-based and training-free paradigms, plus foundational models and toolboxes like Stable Video Diffusion, AnimateDiff, and Open-Sora implementations. Serves as a taxonomy of diffusion architectures—from U-Net and Transformer variants to latent diffusion approaches—enabling researchers to identify methodological patterns across video generation and manipulation applications.
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