ChenHsing/Awesome-Video-Diffusion-Models

A Survey on Video Diffusion Models

(Source: Make-A-Video, SimDA, PYoCo, SVD , Video LDM and Tune-A-Video)

• [News] The updated version is available on arXiv.
• [News] Our survey is accepted by ACM Computing Surveys (CSUR).
• [News] The Chinese translation is available on Zhihu. Special thanks to Dai-Wenxun for this.

Contact

If you have any suggestions or find our work helpful, feel free to contact us

Homepage: Zhen Xing

Email: zhenxingfd@gmail.com

If you find our survey is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@article{xing2023survey,
  title={A survey on video diffusion models},
  author={Xing, Zhen and Feng, Qijun and Chen, Haoran and Dai, Qi and Hu, Han and Xu, Hang and Wu, Zuxuan and Jiang, Yu-Gang},
  journal={ACM Computing Surveys},
  year={2023},
  publisher={ACM New York, NY}
}

Open-source Toolboxes and Foundation Models

| Methods | Task | Github| |:-----:|:-----:|:-----:| | Helios | T2V Generation | | | Movie Gen | T2V Generation | -| | CogVideoX | T2V Generation | | | Open-Sora-Plan | T2V Generation | | | Open-Sora | T2V Generation | | | Morph Studio | T2V Generation | -| | Genie | T2V Generation | -| | Sora | T2V Generation & Editing | -| | VideoPoet | T2V Generation & Editing | -| | Stable Video Diffusion | T2V Generation | | | NeverEnds | T2V Generation | - | | Pika | T2V Generation | - | | EMU-Video | T2V Generation | - | | GEN-2 | T2V Generation & Editing | - | | ModelScope | T2V Generation | | | ZeroScope | T2V Generation | -| | T2V Synthesis Colab | T2V Genetation || | VideoCraft | T2V Genetation & Editing || | Diffusers (T2V synthesis) | T2V Genetation |-| | AnimateDiff | Personalized T2V Genetation || | Text2Video-Zero | T2V Genetation || | HotShot-XL | T2V Genetation || | Genmo | T2V Genetation |-| | Fliki | T2V Generation | -| | Seedream AI Studio | Image Generation + I2V Animation | -| | Omni-Rewriter | Prompt Expansion (Video/Image PE) | |

Table of Contents

• Video Generation
• - Data
• - - Caption-level
• - - Category-level
• - T2V Generation
• - - Training-based
• - - Training-free
• - Video Generation with other Condtions
• - - Pose-gudied
• - - Instruct-guided
• - - Sound-guided
• - - Brain-guided
• - - Multi-Modal guided
• - Unconditional Video Generation
• - - U-Net based
• - - Transformer-based
• - Video Completion
• - - Video Enhance and Restoration
• - - Video Prediction
• Video Editing
• - Text guided Video Editing
• - - Training-based Editing
• - - One-shot Editing
• - - Traning-free
• - Modality-guided Video Editing
• - - Motion-guided
• - - Instruct-guided
• - - Sound-guided
• - - Multi-Modal Control
• - Domain-specific editing
• - Non-diffusion editing
• Video Understanding
• Contact

Video Generation

Data

Caption-level

| Title | arXiv | Github| WebSite | Pub. & Date |:-----:|:-----:|:-----:|:-----:|:-----:| | OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation | || | May, 2025 | | Identity-Preserving Text-to-Video Generation by Frequency Decomposition | || | CVPR, 2025 | |ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation | | | | NeurIPS, 2024 | |Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers | | | | CVPR, 2024 | |CelebV-Text: A Large-Scale Facial Text-Video Dataset| | | - | CVPR, 2023 |InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation | | | - | May, 2023 | |VideoFactory: Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation| | - | - | May, 2023| |Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions | |- |- |Nov, 2021 | | Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval | | - | - |ICCV, 2021 | |MSR-VTT: A Large Video Description Dataset for Bridging Video and Language | | -| -| CVPR, 2016|

| MiniMax H3 1K Prompt Dataset | | | | 2026 |

Category-level

| Title | arXiv | Github| WebSite | Pub. & Date |:-----:|:-----:|:-----:|:-----:|:-----:| |UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild| | - | - | Dec., 2012 |First Order Motion Model for Image Animation | | -|- | May, 2023 | |Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks | | -| -| CVPR,2018|

Metric and BenchMark

| Title | arXiv | Github| WebSite | Pub. & Date |:-----:|:-----:|:-----:|:-----:|:-----:| | OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation | || | May, 2025 | |Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos | | | - | Jul., 2024 | |ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation | | | | NeurIPS, 2024 | | STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models | ||- | ICLR, 2024 | Subjective-Aligned Dateset and Metric for Text-to-Video Quality Assessment | |-|- | Mar, 2024 | Towards A Better Metric for Text-to-Video Generation | |-| | Jan, 2024 |AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI | | - | - | Jan, 2024 | | VBench: Comprehensive Benchmark Suite for Video Generative Models | || | Nov, 2023 | VideoScore2: Think before You Score in Generative Video Evaluation | || | Sep, 2025 |FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation | | - | - | NeurIPS, 2023 | |CVPR 2023 Text Guided Video Editing Competition | | - | - | Oct., 2023 | |EvalCrafter: Benchmarking and Evaluating Large Video Generation Models | | | | Oct., 2023 | |Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset | | - | - | Sep., 2023 |

Text-to-Video Generation

Training-based

| Title | arXiv | Github | WebSite | Pub. & Date | |---|---|---|---|---| | Helios: Real Real-Time Long Video Generation Model | || | Arxiv, 2026 | Identity-Preserving Text-to-Video Generation by Frequency Decomposition | || | CVPR, 2025 | Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning | ||| NeurIPS 2024 | Movie Gen | |-| | Oct, 2024 | CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer | ||-| Oct, 2024 | Grid Diffusion Models for Text-to-Video Generation | || | CVPR, 2024 | MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators | || | Apr., 2024 | Mora: Enabling Generalist Video Generation via A Multi-Agent Framework | |-|- | Mar., 2024 | VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis | |-|- | Mar., 2024 | Genie: Generative Interactive Environments | |-| | Feb., 2024 | Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis | |-| | Feb., 2024 | Lumiere: A Space-Time Diffusion Model for Video Generation | |-| | Jan, 2024 | UNIVG: TOWARDS UNIFIED-MODAL VIDEO GENERATION | |-| | Jan, 2024 | VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models | || | Jan, 2024 | 360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model | |-|[![Website](https://img.shields.io/badge/Websi