# Reangle-A-Video: 4D Video Generation as Video-to-Video Translation

arXiv: https://arxiv.org/abs/2503.09151 · HTML: https://mlsanity.com/paper/2503.09151/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 83 of its 99 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 83)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (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)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [Lumiere: A Space-Time Diffusion Model for Video Generation](https://mlsanity.com/paper/2401.12945/) (2024)
- [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](https://mlsanity.com/paper/2405.10314/) (2024)
- [ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis](https://mlsanity.com/paper/2409.02048/) (2024)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [PixArt-Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://mlsanity.com/paper/2403.04692/) (2024)

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

- [Follow-Your-Creation: Empowering 4D Creation through Video Inpainting](https://mlsanity.com/paper/2506.04590/) (2025)
- [Generative Video Motion Editing with 3D Point Tracks](https://mlsanity.com/paper/2512.02015/) (2025)
- [Taming Video Models for 3D and 4D Generation via Zero-Shot Camera Control](https://mlsanity.com/paper/2509.15130/) (2025)
- [Vista4D: Video Reshooting with 4D Point Clouds](https://mlsanity.com/paper/2604.21915/) (2026)
- [Dynamic View Synthesis as an Inverse Problem](https://mlsanity.com/paper/2506.08004/) (2025)
- [See4D: Pose-Free 4D Generation via Auto-Regressive Video Inpainting](https://mlsanity.com/paper/2510.26796/) (2025)
- [ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding](https://mlsanity.com/paper/2511.19827/) (2025)
- [SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time](https://mlsanity.com/paper/2512.25075/) (2025)
- [WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation](https://mlsanity.com/paper/2511.22098/) (2025)
- [InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem](https://mlsanity.com/paper/2512.05672/) (2025)
- [Full-4D: Generating Full-Scope 4D Scenes from a Single-View Video](https://mlsanity.com/paper/2605.25500/) (2026)
- [HarmoHOI: Harmonizing Appearance and 3D Motion for Multi-view Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2607.17097/) (2026)
- [Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing](https://mlsanity.com/paper/2601.16296/) (2026)
- [Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models](https://mlsanity.com/paper/2605.19398/) (2026)
- [Geo-Align: Video Generation Alignment via Metric Geometry Reward](https://mlsanity.com/paper/2605.23903/) (2026)
- [ProxyPose: 6-DoF Pose Tracking via Video-to-Video Translation](https://mlsanity.com/paper/2607.06555/) (2026)
- [ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling](https://mlsanity.com/paper/2512.01481/) (2025)
- [Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting](https://mlsanity.com/paper/2604.21776/) (2026)
- [MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics](https://mlsanity.com/paper/2605.12119/) (2026)
- [StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation](https://mlsanity.com/paper/2605.21466/) (2026)
- [Track2View: 4D-Consistent Camera-Controlled Video Generation via Paired 3D Point Tracks](https://mlsanity.com/paper/2606.15534/) (2026)
- [SierpinskiCam: Camera-Controlled Video Retaking with Sierpinski Triangle Pattern Cues](https://mlsanity.com/paper/2606.17310/) (2026)
- [TriMotion: Modality-Agnostic Camera Control for Video Generation](https://mlsanity.com/paper/2606.20774/) (2026)
- [MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation](https://mlsanity.com/paper/2606.26087/) (2026)
- [SyncMV4D: Synchronized Multi-view Joint Diffusion of Appearance and Motion for Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2511.19319/) (2025)
- [TrajectoryMover: Generative Movement of Object Trajectories in Videos](https://mlsanity.com/paper/2603.29092/) (2026)
- [Embody4D: A Generalist Data Engine for Embodied 4D World Modeling](https://mlsanity.com/paper/2605.01799/) (2026)
- [FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching](https://mlsanity.com/paper/2605.20910/) (2026)
- [MV-Forcing: Long Multi-View Video Generation via 4D-Grounded Spatio-Temporal Self-Forcing](https://mlsanity.com/paper/2607.05376/) (2026)
- [VS3R: Robust Full-frame Video Stabilization via Deep 3D Reconstruction](https://mlsanity.com/paper/2603.05851/) (2026)

## Abstract (arXiv)

We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos translation, leveraging publicly available image and video diffusion priors. In essence, Reangle-A-Video operates in two stages. (1) Multi-View Motion Learning: An image-to-video diffusion transformer is synchronously fine-tuned in a self-supervised manner to distill view-invariant motion from a set of warped videos. (2) Multi-View Consistent Image-to-Images Translation: The first frame of the input video is warped and inpainted into various camera perspectives under an inference-time cross-view consistency guidance using DUSt3R, generating multi-view consistent starting images. Extensive experiments on static view transport and dynamic camera control show that Reangle-A-Video surpasses existing methods, establishing a new solution for multi-view video generation. We will publicly release our code and data. Project page: https://hyeonho99.github.io/reangle-a-video/