Text2Relight: Creative Portrait Relighting with Text Guidance

• URL: http://arxiv.org/abs/2412.13734v1
• Date: Wed, 18 Dec 2024 11:12:10 GMT
• Title: Text2Relight: Creative Portrait Relighting with Text Guidance
• Authors: Junuk Cha, Mengwei Ren, Krishna Kumar Singh, He Zhang, Yannick Hold-Geoffroy, Seunghyun Yoon, HyunJoon Jung, Jae Shin Yoon, Seungryul Baek,
• Abstract summary: We present a lighting-aware image editing pipeline that, given a portrait image and a text prompt, performs single image relighting.<n>Our model modifies the lighting and color of both the foreground and background to align with the provided text description.
• Score: 26.75526739002697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a lighting-aware image editing pipeline that, given a portrait image and a text prompt, performs single image relighting. Our model modifies the lighting and color of both the foreground and background to align with the provided text description. The unbounded nature in creativeness of a text allows us to describe the lighting of a scene with any sensory features including temperature, emotion, smell, time, and so on. However, the modeling of such mapping between the unbounded text and lighting is extremely challenging due to the lack of dataset where there exists no scalable data that provides large pairs of text and relighting, and therefore, current text-driven image editing models does not generalize to lighting-specific use cases. We overcome this problem by introducing a novel data synthesis pipeline: First, diverse and creative text prompts that describe the scenes with various lighting are automatically generated under a crafted hierarchy using a large language model (*e.g.,* ChatGPT). A text-guided image generation model creates a lighting image that best matches the text. As a condition of the lighting images, we perform image-based relighting for both foreground and background using a single portrait image or a set of OLAT (One-Light-at-A-Time) images captured from lightstage system. Particularly for the background relighting, we represent the lighting image as a set of point lights and transfer them to other background images. A generative diffusion model learns the synthesized large-scale data with auxiliary task augmentation (*e.g.,* portrait delighting and light positioning) to correlate the latent text and lighting distribution for text-guided portrait relighting.

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