@inproceedings{
chen2024mvsplat,
title={{MVS}plat360: Feed-Forward 360 Scene Synthesis from Sparse Views},
author={Yuedong Chen and Chuanxia Zheng and Haofei Xu and Bohan Zhuang and Andrea Vedaldi and Tat-Jen Cham and Jianfei Cai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=B0OWOkMwhz}
}
We introduce MVSplat360, a feed-forward approach for 360° novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-posed due to minimal overlap among input views and insufficient visual information provided, making it challenging for conventional methods to achieve high-quality results. Our MVSplat360 addresses this by effectively combining geometry-aware 3D reconstruction with temporally consistent video generation. Specifically, it refactors a feed-forward 3D Gaussian Splatting (3DGS) model to render features directly into the latent space of a pre-trained Stable Video Diffusion (SVD) model, where these features then act as pose and visual cues to guide the denoising process and produce photorealistic 3D-consistent views. Our model is end-to-end trainable and supports rendering arbitrary views with as few as 5 sparse input views. To evaluate MVSplat360's performance, we introduce a new benchmark using the challenging DL3DV-10K dataset, where MVSplat360 achieves superior visual quality compared to state-of-the-art methods on wide-sweeping or even 360° NVS tasks. Experiments on the existing benchmark RealEstate10K also confirm the effectiveness of our model. Readers are highly recommended to view the video results at [donydchen.github.io/mvsplat360](https://donydchen.github.io/mvsplat360).
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views
Yuedong Chen Chuanxia Zheng Haofei Xu Bohan Zhuang Andrea Vedaldi Tat-Jen Cham Jianfei Cai