This repository will contain the official implementation of the paper: NeoMap: Training-free Novel-View Synthesis from Single Images and Videos. NeoMap is a training-free framework for novel-view video synthesis from single images or monocular videos. It locates high-fidelity, view-consistent novel-view solutions within the natural video data manifold learned by general pre-trained video models through convergent manifold alternating projection iterations.
Please feel free to contact us via [jinxi.li, tonax.zhang]@connect.polyu.hk or open an issue if you have any questions or suggestions.
- 2026-07-02: NeoMap is released on arXiv.
- 2026-07-02: NeoMap is accepted by ECCV 2026.
- Submit the paper onto arXiv.
- Release the project repository.
- Release installation instructions.
- Release inference code.
- Release evaluation code.
- Release data and pretrained-model instructions.
We thank the authors of FlexWorld, Wan2.2, VGGT, Video Depth Anything, and VIPE for their open-source code and excellent work.
If you find our work helpful, please consider citing:
@article{li2026neomap,
title={NeoMap: Training-free Novel-View Synthesis from Single Images and Videos},
author={Jinxi Li and Tianyi Zhang and Yafei Yang and Zihui Zhang and Peng Huang and Koon Wing Macgyver Lin and Bo Yang},
journal={ECCV},
year={2026}
}