Adjustable Visual Appearance for Generalizable Novel View Synthesis
Paper in proceeding, 2025

We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition, without any scene specific training or access to reference views at the target condition. Our method is based on a pretrained generalizable transformer architecture and is fine-tuned on synthetically generated scenes under different appearance conditions. This allows for rendering novel views in a consistent manner for 3D scenes that were not included in the training set, along with the ability to (i) modify their appearance to match the target condition and (ii) smoothly interpolate between different conditions. Experiments on real and synthetic scenes show that our method is able to generate 3D consistent renderings while making realistic appearance changes, including qualitative and quantitative comparisons. Please refer to our project page for video results: https://ava-nvs.github.io.

NeRFs

Generalizable Novel View Synthesis

3D Style Transfer

Author

Josef Bengtson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

David Nilsson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Che-Tsung Lin

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Marcel Büsching

Royal Institute of Technology (KTH)

Fredrik Kahl

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14892 LNCS 157-171
9789819787012 (ISBN)

4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024
Jeju Island, South Korea,

Subject Categories (SSIF 2025)

Computer graphics and computer vision

Signal Processing

Infrastructure

Chalmers e-Commons (incl. C3SE, 2020-)

DOI

10.1007/978-981-97-8702-9_11

More information

Latest update

3/14/2025