Conference item
MVSplat360: benchmarking 360 generalizable 3D novel view synthesis from sparse views
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/Z001811/1
- Programme:
- SYN3D
- Publisher:
- Curran Associates
- Host title:
- Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
- Volume:
- 2
- Pages:
- 107064-107086
- Publication date:
- 2025-02-01
- Acceptance date:
- 2024-09-25
- Event title:
- 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
- Event location:
- Vancouver, BC, Canada
- Event website:
- https://neurips.cc/Conferences/2024
- Event start date:
- 2024-12-10
- Event end date:
- 2024-12-15
- ISSN:
-
1049-5258
- ISBN:
- 9798331314385
- Language:
-
English
- Pubs id:
-
2081409
- Local pid:
-
pubs:2081409
- Deposit date:
-
2025-01-29
Terms of use
- Copyright holder:
- Chen et al. and NeurIPS
- Copyright date:
- 2025
- Rights statement:
- © (2025) by individual authors and Neural Information Processing Systems Foundation Inc. All rights reserved.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available from the Neural Information Processing Systems Foundation at: https://proceedings.neurips.cc/paper_files/paper/2024/hash/c196239c5f9481e0db2755f31fe4585f-Abstract-Conference.html
If you are the owner of this record, you can report an update to it here: Report update to this record