ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916976940548096 |
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| author | Xiao, Jiuhong Nayak, Roshan Zhang, Ning Tortei, Daniel Loianno, Giuseppe |
| author_facet | Xiao, Jiuhong Nayak, Roshan Zhang, Ning Tortei, Daniel Loianno, Giuseppe |
| contents | Paired RGB-thermal data is crucial for visual-thermal sensor fusion and cross-modality tasks, including important applications such as multi-modal image alignment and retrieval. However, the scarcity of synchronized and calibrated RGB-thermal image pairs presents a major obstacle to progress in these areas. To overcome this challenge, RGB-to-Thermal (RGB-T) image translation has emerged as a promising solution, enabling the synthesis of thermal images from abundant RGB datasets for training purposes. In this study, we propose ThermalGen, an adaptive flow-based generative model for RGB-T image translation, incorporating an RGB image conditioning architecture and a style-disentangled mechanism. To support large-scale training, we curated eight public satellite-aerial, aerial, and ground RGB-T paired datasets, and introduced three new large-scale satellite-aerial RGB-T datasets--DJI-day, Bosonplus-day, and Bosonplus-night--captured across diverse times, sensor types, and geographic regions. Extensive evaluations across multiple RGB-T benchmarks demonstrate that ThermalGen achieves comparable or superior translation performance compared to existing GAN-based and diffusion-based methods. To our knowledge, ThermalGen is the first RGB-T image translation model capable of synthesizing thermal images that reflect significant variations in viewpoints, sensor characteristics, and environmental conditions. Project page: http://xjh19971.github.io/ThermalGen |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_24878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation Xiao, Jiuhong Nayak, Roshan Zhang, Ning Tortei, Daniel Loianno, Giuseppe Computer Vision and Pattern Recognition Robotics Paired RGB-thermal data is crucial for visual-thermal sensor fusion and cross-modality tasks, including important applications such as multi-modal image alignment and retrieval. However, the scarcity of synchronized and calibrated RGB-thermal image pairs presents a major obstacle to progress in these areas. To overcome this challenge, RGB-to-Thermal (RGB-T) image translation has emerged as a promising solution, enabling the synthesis of thermal images from abundant RGB datasets for training purposes. In this study, we propose ThermalGen, an adaptive flow-based generative model for RGB-T image translation, incorporating an RGB image conditioning architecture and a style-disentangled mechanism. To support large-scale training, we curated eight public satellite-aerial, aerial, and ground RGB-T paired datasets, and introduced three new large-scale satellite-aerial RGB-T datasets--DJI-day, Bosonplus-day, and Bosonplus-night--captured across diverse times, sensor types, and geographic regions. Extensive evaluations across multiple RGB-T benchmarks demonstrate that ThermalGen achieves comparable or superior translation performance compared to existing GAN-based and diffusion-based methods. To our knowledge, ThermalGen is the first RGB-T image translation model capable of synthesizing thermal images that reflect significant variations in viewpoints, sensor characteristics, and environmental conditions. Project page: http://xjh19971.github.io/ThermalGen |
| title | ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2509.24878 |