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README.md

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+---
+pipeline_tag: video-to-video
+tags:
+  - video
+  - video-reconstruction
+  - video-generation
+  - video-representation
+  - v-rae
+  - rae
+  - pytorch
+  - arxiv:2608.13556
+datasets:
+  - quchenyuan/UCF101-ZIP
+---
+
+<div align="center">
+<h1>V-RAE: Rethinking Video Latent Spaces for Generation</h1>
+<p>
+  <a href="https://guominghui07.github.io/">Minghui Guo</a><sup>1</sup> &nbsp;&nbsp; <a href="https://sqwu.top/">Shengqiong Wu</a><sup>2</sup> &nbsp;&nbsp; <a href="https://haofei.vip/">Hao Fei</a><sup>2</sup>
+</p>
+<p>
+  <sup>1</sup>National University of Singapore &nbsp;&nbsp; <sup>2</sup>University of Oxford
+</p>
+</div>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2608.13556"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b" alt="Paper"></a>
+  <a href="https://v-rae.github.io/"><img src="https://img.shields.io/badge/Homepage-Project%20Page-blue" alt="Project Page"></a>
+  <a href="https://github.com/V-RAE/V-RAE"><img src="https://img.shields.io/badge/Code-GitHub-black" alt="Code"></a>
+</p>
+
+V-RAE is a video representation autoencoder that builds compact generative
+latents on top of frozen vision foundation model representations. It uses a
+lightweight temporal pooling module to remove temporal redundancy while
+preserving semantic structure, together with a video decoder for reconstructing
+continuous motion.
+
+![V-RAE method](https://raw.githubusercontent.com/V-RAE/V-RAE/main/assets/V-RAE.png)
+
+![V-RAE overview](https://raw.githubusercontent.com/V-RAE/V-RAE/main/assets/V-RAE-overall.png)
+
+## Released Models
+
+This repository provides V-RAE checkpoints based on four frozen visual encoders:
+
+| Checkpoint | Encoder |
+| --- | --- |
+| `vrae/vrae_dinov3.pt` | DINOv3 ViT-L/16 |
+| `vrae/vrae_siglip2.pt` | SigLIP2 ViT-L/16 |
+| `vrae/vrae_vjepa2.1.pt` | V-JEPA2.1 ViT-L/16 |
+| `vrae/vrae_eupe.pt` | EUPE ViT-B/16 |
+
+Class-conditional VideoDiT checkpoints and their matching latent statistics are
+also provided for UCF101 and Kinetics-600:
+
+- UCF101: V-JEPA2.1 and EUPE variants
+- Kinetics-600: V-JEPA2.1 variant
+
+## Quick Start
+
+Install the official implementation:
+
+```bash
+git clone https://github.com/V-RAE/V-RAE.git
+cd V-RAE
+
+conda create -n vrae python=3.10 -y
+conda activate vrae
+conda install -c conda-forge ffmpeg -y
+
+pip install uv
+uv pip install -e .
+```
+
+Download the released checkpoints:
+
+```bash
+hf download Guomh0707/V-RAE-Models --local-dir ckpts
+```
+
+Download the matching frozen encoder by following the
+[encoder instructions](https://github.com/V-RAE/V-RAE/blob/main/third_party/README.md#download-pre-trained-encoder-weights).
+
+Place three input videos at `assets/sample1.mp4`, `assets/sample2.mp4`, and
+`assets/sample3.mp4`, then run one of:
+
+```bash
+python sampling.py dino
+python sampling.py siglip
+python sampling.py vjepa
+python sampling.py eupe
+```
+
+Reconstruction results are saved under `outputs/<variant>/`.
+
+## Results
+
+| Evaluation | Result |
+| --- | ---: |
+| Kinetics-600 reconstruction | **2.13 rFVD** |
+| UCF101 class-conditional generation | **117.86 gFVD** |
+| Kinetics-600 class-conditional generation | **19.16 gFVD** |
+
+
+## Notes
+
+- Each V-RAE checkpoint requires its corresponding frozen encoder weights.
+- These are custom PyTorch checkpoints and require the official V-RAE codebase.
+- This repository is intended primarily for research use.
+
+## Citation
+
+```bibtex
+@article{guo2026vrae,
+  title   = {V-RAE: Rethinking Video Latent Spaces for Generation},
+  author  = {Guo, Minghui and Wu, Shengqiong and Fei, Hao},
+  journal = {arXiv preprint arXiv:2608.13556},
+  year    = {2026},
+}
+```