Lite2Relight: 3D-aware Single Image Portrait Relighting

TL;DR

Lite2Relight is a novel method that enables photorealistic, 3D-consistent portrait relighting and view synthesis from a single image at interactive speeds, improving over prior methods in realism and generalization.

Contribution

It introduces a new approach extending EG3D with a lightstage dataset and a geometry-aware encoder for high-quality, 3D-consistent relighting from a single image.

Findings

Outperforms state-of-the-art in realism and efficacy

Produces 3D-consistent results including hair and expressions

Operates at interactive speeds for practical applications

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. Project page: https://vcai.mpi-inf.mpg.de/projects/Lite2Relight/