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Beeble Researchers Attempt to Manipulate Lighting on Digital Image Using AI

Written by @autoencoder | Published on 2024/12/21
TL;DR —
Researchers at Beeble AI have developed a method for improving how light and shadows can be applied to human portraits in digital images.

Authors:

(1) Hoon Kim, Beeble AI, and contributed equally to this work;

(2) Minje Jang, Beeble AI, and contributed equally to this work;

(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;

(4) Jisoo Lee, Beeble AI, and contributed equally to this work;

(5) Donghyun Na, Beeble AI, and contributed equally to this work;

(6) Sanghyun Woo, New York University, and contributed equally to this work.

Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below.

Appendix

3.2. Problem Formulation

Figure 2. SwitchLight Architecture. The input source image is decomposed into normal map, lighting, diffuse and specular components. Given these intrinsics, images are re-rendered under target lighting. The architecture integrates the Cook-Torrance reflection model; the final output combines physically-based predictions with neural network enhancements for realistic portrait relighting.

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

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Written by
@autoencoder
Research & publications on Auto Encoders, revolutionizing data compression and feature learning techniques.
Topics and
tags
self-supervised-learning|relighting|human-portrait-relighting|physics-guided-architecture|cook-torrance-model|light-surface-interactions|switchlight-framework|self-supervised-pre-training
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