Beeble Researchers Develop AI That Can Make Any Photo Look Perfectly Lit—Even in the Darkest Room

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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.

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Editor’s Note: This is Part 6 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.

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Table of Links

• Abstract and 1. Introduction
• 2. Related Work
• 3. SwitchLight and 3.1. Preliminaries
• 3.2. Problem Formulation
• 3.3. Architecture
• 3.4. Objectives
• 4. Multi-Masked Autoencoder Pre-training
• 5. Data
• 6. Experiments
• 7. Conclusion

Appendix

• A. Implementation Details
• B. User Study Interface
• C. Video Demonstration
• D. Additional Qualitative Results & References

3.4. Objectives

We supervise both intrinsic image attributes and relit images using their corresponding ground truths, obtained from the lightstage. We employ a combination of reconstruction, perceptual [24], adversarial [22], and specular [34] losses.

Final Loss. The SwitchLight is trained in an end-to-end manner using the weighted sum of the above losses:

We empirically determined the weighting coefficients.

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

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