# EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation

arXiv: https://arxiv.org/abs/2411.08380 · HTML: https://mlsanity.com/paper/2411.08380/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 85 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 30 of 71)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation](https://mlsanity.com/paper/2406.06525/) (2024)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation](https://mlsanity.com/paper/2310.05737/) (2023)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation](https://mlsanity.com/paper/2307.06942/) (2023)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)

## Built on by (most cited first, 30 of 41)

- [EgoLife: Towards Egocentric Life Assistant](https://mlsanity.com/paper/2503.03803/) (2025)
- [ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration](https://mlsanity.com/paper/2411.19548/) (2024)
- [Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2508.13073/) (2025)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning](https://mlsanity.com/paper/2505.17006/) (2025)
- [ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation](https://mlsanity.com/paper/2503.18438/) (2025)
- [MosaicMem: Hybrid Spatial Memory for Controllable Video World Models](https://mlsanity.com/paper/2603.17117/) (2026)
- [RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation](https://mlsanity.com/paper/2509.15212/) (2025)
- [A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation](https://mlsanity.com/paper/2602.01067/) (2026)
- [PlayerOne: Egocentric World Simulator](https://mlsanity.com/paper/2506.09995/) (2025)
- [HumanDreamer: Generating Controllable Human-Motion Videos via Decoupled Generation](https://mlsanity.com/paper/2503.24026/) (2025)
- [FreeLong++: Training-Free Long Video Generation via Multi-band SpectralFusion](https://mlsanity.com/paper/2507.00162/) (2025)
- [Precise Action-to-Video Generation Through Visual Action Prompts](https://mlsanity.com/paper/2508.13104/) (2025)
- [OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction](https://mlsanity.com/paper/2512.16842/) (2025)
- [EgoSim: Egocentric World Simulator for Embodied Interaction Generation](https://mlsanity.com/paper/2604.01001/) (2026)
- [Action100M: A Large-scale Video Action Dataset](https://mlsanity.com/paper/2601.10592/) (2026)
- [EgoForge: Goal-Directed Egocentric World Simulator](https://mlsanity.com/paper/2603.20169/) (2026)
- [World Action Models: A Survey](https://mlsanity.com/paper/2606.20781/) (2026)
- [StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation](https://mlsanity.com/paper/2510.05057/) (2025)
- [Mask2IV: Interaction-Centric Video Generation via Mask Trajectories](https://mlsanity.com/paper/2510.03135/) (2025)
- [EgoM2P: Egocentric Multimodal Multitask Pretraining](https://mlsanity.com/paper/2506.07886/) (2025)
- [Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset](https://mlsanity.com/paper/2506.04224/) (2025)
- [HANDI: Hand-Centric Text-and-Image Conditioned Video Generation](https://mlsanity.com/paper/2412.04189/) (2024)
- [EgoLCD: Egocentric Video Generation with Long Context Diffusion](https://mlsanity.com/paper/2512.04515/) (2025)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [NymeriaPlus: Enriching Nymeria Dataset with Additional Annotations and Data](https://mlsanity.com/paper/2603.18496/) (2026)
- [EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks](https://mlsanity.com/paper/2604.09535/) (2026)
- [DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models](https://mlsanity.com/paper/2505.07057/) (2025)
- [E3C: Video Generation with 3D Environmental Memory and Ego-Exo Human Pose Control](https://mlsanity.com/paper/2605.26316/) (2026)
- [HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control](https://mlsanity.com/paper/2607.02075/) (2026)

## Abstract (arXiv)

Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egocentric videos presents substantial challenges due to the dynamic nature of egocentric viewpoints, the intricate diversity of actions, and the complex variety of scenes encountered. Existing datasets are inadequate for addressing these challenges effectively. To bridge this gap, we present EgoVid-5M, the first high-quality dataset specifically curated for egocentric video generation. EgoVid-5M encompasses 5 million egocentric video clips and is enriched with detailed action annotations, including fine-grained kinematic control and high-level textual descriptions. To ensure the integrity and usability of the dataset, we implement a sophisticated data cleaning pipeline designed to maintain frame consistency, action coherence, and motion smoothness under egocentric conditions. Furthermore, we introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals. The EgoVid-5M dataset, associated action annotations, and all data cleansing metadata will be released for the advancement of research in egocentric video generation.