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Lite2Relight: 3D-aware Single Image Portrait Relighting

Rao, Pramod; Fox, Gereon; Meka, Abhimitra; Mallikarjun, BR; Zhan, Fangneng; Weyrich, Tim; Bickel, Bernd; ... Theobalt, Christian; + view all (2024) Lite2Relight: 3D-aware Single Image Portrait Relighting. In: Burbano, Andres and Zorin, Denis and Jarosz, Wojciech, (eds.) SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers. Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

Achieving photorealistic 3D view synthesis and relighting of human portraits is pivotal for advancing AR/VR applications. Existing methodologies in portrait relighting demonstrate substantial limitations in terms of generalization and 3D consistency, coupled with inaccuracies in physically realistic lighting and identity preservation. Furthermore, personalization from a single view is difficult to achieve and often requires multiview images during the testing phase or involves slow optimization processes. This paper introduces Lite2Relight, a novel technique that can predict 3D consistent head poses of portraits while performing physically plausible light editing at interactive speed. Our method uniquely extends the generative capabilities and efficient volumetric representation of EG3D, leveraging a lightstage dataset to implicitly disentangle face reflectance and perform relighting under target HDRI environment maps. By utilizing a pre-trained geometry-aware encoder and a feature alignment module, we map input images into a relightable 3D space, enhancing them with a strong face geometry and reflectance prior. Through extensive quantitative and qualitative evaluations, we show that our method outperforms the state-of-the-art methods in terms of efficacy, photorealism, and practical application. This includes producing 3D-consistent results of the full head, including hair, eyes, and expressions. Lite2Relight paves the way for large-scale adoption of photorealistic portrait editing in various domains, offering a robust, interactive solution to a previously constrained problem.

Type: Proceedings paper
Title: Lite2Relight: 3D-aware Single Image Portrait Relighting
Event: SIGGRAPH Conference on Emerging Technologies
Location: CO, Denver
Dates: 28 Jul 2024 - 1 Aug 2024
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3641519.3657470
Publisher version: https://doi.org/10.1145/3641519.3657470
Language: English
Additional information: This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: Faces, Relighting, Volumetric Representation, Generative Modeling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10205201
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