TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation

• URL: http://arxiv.org/abs/2602.19430v2
• Date: Tue, 24 Feb 2026 03:51:01 GMT
• Title: TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation
• Authors: Dong-Guw Lee, Tai Hyoung Rhee, Hyunsoo Jang, Young-Sik Shin, Ukcheol Shin, Ayoung Kim,
• Abstract summary: TherA is a controllable RGB-to-TIR translation framework that produces diverse and thermally plausible images at both scene and object level.<n>TherA achieves state-of-the-art translation performance, demonstrating improved zero-shot translation performance up to 33% increase averaged across all metrics.
• Score: 12.591408054941027
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 TIR synthesis and fine-grained control across time of day, weather, and object state. Compared to other baselines, TherA achieves state-of-the-art translation performance, demonstrating improved zero-shot translation performance up to 33% increase averaged across all metrics.

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