Generative Image Dynamics
Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander Holynski
Abstract
Input Picture X coefficients Spectral Volume (Image-Space Modal Basis) 0.2Hz 0.4Hz 3.0Hz Looping video Interactive dynamics … Y coefficients Figure 1. We model a generative image-space prior on scene motion: from a single RGB image, our method generates a spectral volume [23], a motion representation that models dense, long-term pixel trajectories in the Fourier domain. Our learned motion priors can be used to turn a single picture into a seamlessly looping video, or into an interactive simulation of dynamics that responds to user inputs like dragging and releasing points. On the right, we visualize output videos as space-time X-t slices (along the input scanline shown on the left).
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7a78c80f-4bb3-419a-a648-57fcb77cf170Cited by top-tier papers50
- Learning Interactive Real-World SimulatorsSherry Yang, Yilun Du, Seyed Kamyar Seyed Ghasemipour, Jonathan Tompson et al.ICLR 2024 · 399 citations
- FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editingYuren Cong, Mengmeng Xu, Christian Simon, Shoufa Chen et al.ICLR 2024 · 175 citations
- MotionStream: Real-Time Video Generation with Interactive Motion ControlsJoonghyuk Shin, Zhengqi Li, Richard Zhang, Jun-Yan Zhu et al.ICLR 2026 · 79 citations
- GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative ModelingBowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang et al.NeurIPS 2024 · 49 citations
- NewtonGen: Physics-consistent and Controllable Text-to-Video Generation via Neural Newtonian DynamicsYu Yuan, Xijun Wang, Tharindu Wickremasinghe, Zeeshan Nadir et al.ICLR 2026 · 46 citations
Builds on43
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li et al.NeurIPS 2022 · 8,965 citations
- AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific TuningYuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang et al.ICLR 2024 · 1,493 citations
Related papers
- Animating Pictures With Eulerian Motion FieldsAleksander Holynski, Brian L. Curless, Steven M. Seitz, Richard SzeliskiCVPR 2021
- Endless loops: detecting and animating periodic patterns in still imagesTavi Halperin, Hanit Hakim, Orestis Vantzos, Gershon Hochman et al.SIGGRAPH 2021 · 14 citations
- Blowing in the Wind: CycleNet for Human Cinemagraphs from Still ImagesHugo Bertiche, Niloy J. Mitra, Kuldeep Kulkarni, Chun-Hao Paul Huang et al.CVPR 2023
- Motion-Adjustable Neural Implicit Video RepresentationLong Mai, Feng LiuCVPR 2022 · 20 citations
- Animating General Image with Large Visual Motion ModelDengsheng Chen, Xiaoming Wei, Xiaolin WeiCVPR 2024
