ThermalGen: Style-Disentangled Flow-Based Generative Models for
  RGB-to-Thermal Image Translation
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
68dc12914159d1f2418f98c5
Jiuhong Xiao
claimed_verified
2025-10-01T10:23:03.794Z
664c140e0156306f1c705df1
/avatars/5b6a3761abee8c9378823832021053cd.svg
Jiuhong
user
xjh19972
68dc12914159d1f2418f98c6
Roshan Nayak
68dc12914159d1f2418f98c7
Ning Zhang
68dc12914159d1f2418f98c8
Daniel Tortei
68dc12914159d1f2418f98c9
Giuseppe Loianno
ThermalGen: Style-Disentangled Flow-Based Generative Models for
  RGB-to-Thermal Image Translation
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
ThermalGen, an adaptive flow-based generative model with RGB image conditioning and style-disentangled mechanism, synthesizes thermal images from RGB datasets, achieving superior performance across various benchmarks.
flow-based generative model RGB image conditioning style-disentangled mechanism RGB-T image translation GAN-based methods diffusion-based methods