TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation
Dong-Guw Lee, Tai Hyoung Rhee, Hyunsoo Jang, Young-Sik Shin, Ukcheol Shin, Ayoung Kim

TL;DR
TherA is a novel controllable RGB-to-thermal infrared translation framework that synthesizes thermally plausible images with fine-grained control, overcoming limitations of RGB-centric priors and enhancing zero-shot translation performance.
Contribution
TherA introduces a thermal-aware embedding and a diffusion-based translator for realistic, controllable TIR image synthesis from RGB images, incorporating scene and object heat context.
Findings
Achieves state-of-the-art translation performance
Improves zero-shot translation by up to 33%
Enables fine-grained control over synthesis parameters
Abstract
Despite the inherent advantages of thermal infrared(TIR) imaging, large-scale data collection and annotation remain a major bottleneck for TIR-based perception. A practical alternative is to synthesize pseudo TIR data via image translation; however, most RGB-to-TIR approaches heavily rely on RGB-centric priors that overlook thermal physics, yielding implausible heat distributions. In this paper, we introduce TherA, a controllable RGB-to-TIR translation framework that produces diverse and thermally plausible images at both scene and object level. TherA couples TherA-VLM with a latent-diffusion-based translator. Given a single RGB image and a user-prompted condition pair, TherA-VLM yields a thermal-aware embedding that encodes scene, object, material, and heat-emission context reflecting the input scene-condition pair. Conditioning the diffusion model on this embedding enables realistic…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Neural Network Applications · Image Enhancement Techniques
