# AnimateAnything: Consistent and Controllable Animation for Video Generation

arXiv: https://arxiv.org/abs/2411.10836 · HTML: https://mlsanity.com/paper/2411.10836/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 61 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 30 of 49)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (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)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://mlsanity.com/paper/2306.02018/) (2023)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [Pyramidal Flow Matching for Efficient Video Generative Modeling](https://mlsanity.com/paper/2410.05954/) (2024)
- [OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation](https://mlsanity.com/paper/2407.02371/) (2024)
- [Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering](https://mlsanity.com/paper/2106.02634/) (2021)
- [ControlVideo: Training-free Controllable Text-to-Video Generation](https://mlsanity.com/paper/2305.13077/) (2023)
- [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023)
- [Unifying Flow, Stereo and Depth Estimation](https://mlsanity.com/paper/2211.05783/) (2022)

## Built on by (most cited first, 30 of 35)

- [Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control](https://mlsanity.com/paper/2501.03847/) (2025)
- [AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers](https://mlsanity.com/paper/2411.18673/) (2024)
- [MotionStream: Real-Time Video Generation with Interactive Motion Controls](https://mlsanity.com/paper/2511.01266/) (2025)
- [Towards Understanding Camera Motions in Any Video](https://mlsanity.com/paper/2504.15376/) (2025)
- [FullDiT: Multi-Task Video Generative Foundation Model with Full Attention](https://mlsanity.com/paper/2503.19907/) (2025)
- [Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation](https://mlsanity.com/paper/2509.19296/) (2025)
- [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://mlsanity.com/paper/2503.21765/) (2025)
- [Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation](https://mlsanity.com/paper/2508.07981/) (2025)
- [Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation](https://mlsanity.com/paper/2501.05020/) (2025)
- [Video-As-Prompt: Unified Semantic Control for Video Generation](https://mlsanity.com/paper/2510.20888/) (2025)
- [Motion Modes: What Could Happen Next?](https://mlsanity.com/paper/2412.00148/) (2024)
- [EchoVideo: Identity-Preserving Human Video Generation by Multimodal Feature Fusion](https://mlsanity.com/paper/2501.13452/) (2025)
- [MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent](https://mlsanity.com/paper/2502.03207/) (2025)
- [AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models](https://mlsanity.com/paper/2505.20255/) (2025)
- [IDCNet: Guided Video Diffusion for Metric-Consistent RGBD Scene Generation with Precise Camera Control](https://mlsanity.com/paper/2508.04147/) (2025)
- [MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation](https://mlsanity.com/paper/2509.26391/) (2025)
- [The World is Your Canvas: Painting Promptable Events with Reference Images, Trajectories, and Text](https://mlsanity.com/paper/2512.16924/) (2025)
- [Future Optical Flow Prediction Improves Robot Control & Video Generation](https://mlsanity.com/paper/2601.10781/) (2026)
- [DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation](https://mlsanity.com/paper/2503.06053/) (2025)
- [Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!](https://mlsanity.com/paper/2510.03550/) (2025)
- [V-Warper: Appearance-Consistent Video Diffusion Personalization via Value Warping](https://mlsanity.com/paper/2512.12375/) (2025)
- [Learning to Generate Rigid Body Interactions with Video Diffusion Models](https://mlsanity.com/paper/2510.02284/) (2025)
- [UniMMVSR: A Unified Multi-Modal Framework for Cascaded Video Super-Resolution](https://mlsanity.com/paper/2510.08143/) (2025)
- [Consistent text-to-image generation via scene de-contextualization](https://mlsanity.com/paper/2510.14553/) (2025)
- [Focal Guidance: Unlocking Controllability from Semantic-Weak Layers in Video Diffusion Models](https://mlsanity.com/paper/2601.07287/) (2026)
- [MotionSpec: Spectral Trajectory Supervision for Motion-Consistent Video Generation](https://mlsanity.com/paper/2609.28095/) (2026)
- [FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation](https://mlsanity.com/paper/2604.17625/) (2026)
- [MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control](https://mlsanity.com/paper/2606.29473/) (2026)
- [GraphVid: Interactive Graph-Controllable Video Generation](https://mlsanity.com/paper/2607.21580/) (2026)
- [FlexComposer: Unified Video Compositing from Images to Dynamic Footage with Flexible Trajectory Control](https://mlsanity.com/paper/2607.29627/) (2026)

## Abstract (arXiv)

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations. Specifically, we carefully design a multi-scale control feature fusion network to construct a common motion representation for different conditions. It explicitly converts all control information into frame-by-frame optical flows. Then we incorporate the optical flows as motion priors to guide final video generation. In addition, to reduce the flickering issues caused by large-scale motion, we propose a frequency-based stabilization module. It can enhance temporal coherence by ensuring the video's frequency domain consistency. Experiments demonstrate that our method outperforms the state-of-the-art approaches. For more details and videos, please refer to the webpage: https://yu-shaonian.github.io/Animate_Anything/.