TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914350597406720 |
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| author | Lee, Dong-Guw Rhee, Tai Hyoung Jang, Hyunsoo Shin, Young-Sik Shin, Ukcheol Kim, Ayoung |
| author_facet | Lee, Dong-Guw Rhee, Tai Hyoung Jang, Hyunsoo Shin, Young-Sik Shin, Ukcheol Kim, Ayoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19430 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation Lee, Dong-Guw Rhee, Tai Hyoung Jang, Hyunsoo Shin, Young-Sik Shin, Ukcheol Kim, Ayoung Computer Vision and Pattern Recognition 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. |
| title | TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.19430 |