Low-Rank Adaptation of Neural Fields
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3757377.3763882.pdf
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200.74 MB
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Author(s)
Truong, Anh
Mahmoud, Ahmed
Konakovi? Lukovi?, Mina
Solomon, Justin
Date Issued
December 14, 2025
Publisher
ACM|SIGGRAPH Asia 2025 Conference Papers
Citation
Anh Truong, Ahmed H. Mahmoud, Mina Konaković Luković, and Justin Solomon. 2025. Low-Rank Adaptation of Neural Fields. In Proceedings of the SIGGRAPH Asia 2025 Conference Papers (SA Conference Papers '25). Association for Computing Machinery, New York, NY, USA, Article 86, 1–12.
Version
Final published version
Abstract
Processing visual data often involves small adjustments or sequences of changes, e.g., image filtering, surface smoothing, and animation. While established graphics techniques like normal mapping and video compression exploit redundancy to encode such small changes efficiently, the problem of encoding small changes to neural fields—neural network parameterizations of visual or physical functions—has received less attention. We propose a parameter-efficient strategy for updating neural fields using low-rank adaptations (LoRA). LoRA, a method from the parameter-efficient fine-tuning LLM community, encodes small updates to pre-trained models with minimal computational overhead. We adapt LoRA for instance-specific neural fields, avoiding the need for large pre-trained models and yielding lightweight updates. We validate our approach with experiments in image filtering, geometry editing, video compression, and energy-based editing, demonstrating its effectiveness and versatility for representing neural field updates.
Description
Anh Truong, Ahmed H. Mahmoud, Mina Konaković Luković, and Justin Solomon. 2025. Low-Rank Adaptation of Neural Fields. In Proceedings of the SIGGRAPH Asia 2025 Conference Papers (SA Conference Papers '25). Association for Computing Machinery, New York, NY, USA, Article 86, 1–12.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3757377.3763882
