LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images

• URL: http://arxiv.org/abs/2503.16376v1
• Date: Thu, 20 Mar 2025 17:39:06 GMT
• Title: LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images
• Authors: Leyang Wang, Joice Lin,
• Abstract summary: We propose a framework called LaPIG, which enables the construction of high-quality paired visible and thermal images using captions generated by Large Language Models (LLMs)<n>Our approach not only generates multi-view paired visible and thermal images to increase data diversity but also produces high-quality paired data while maintaining their identity information.<n>We evaluate our method on public datasets by comparing it with existing methods, demonstrating the superiority of LaPIG.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of modern machine learning, particularly in facial translation networks, is highly dependent on the availability of high-quality, paired, large-scale datasets. However, acquiring sufficient data is often challenging and costly. Inspired by the recent success of diffusion models in high-quality image synthesis and advancements in Large Language Models (LLMs), we propose a novel framework called LLM-assisted Paired Image Generation (LaPIG). This framework enables the construction of comprehensive, high-quality paired visible and thermal images using captions generated by LLMs. Our method encompasses three parts: visible image synthesis with ArcFace embedding, thermal image translation using Latent Diffusion Models (LDMs), and caption generation with LLMs. Our approach not only generates multi-view paired visible and thermal images to increase data diversity but also produces high-quality paired data while maintaining their identity information. We evaluate our method on public datasets by comparing it with existing methods, demonstrating the superiority of LaPIG.

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