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Human vision is often adversely affected by complex environmental factors,especially in night vision scenarios. Thus, infrared cameras are oftenleveraged to help enhance the visual effects via detecting infrared radiationin the surrounding environment, but the infrared videos are undesirable due tothe lack of detailed semantic information. In such a case, an effectivevideo-to-video translation method from the infrared domain to the visible lightcounterpart is strongly needed by overcoming the intrinsic huge gap betweeninfrared and visible fields. To address this challenging problem, we propose aninfrared-to-visible (I2V) video translation method I2V-GAN to generatefine-grained and spatial-temporal consistent visible light videos by givenunpaired infrared videos. Technically, our model capitalizes on three types ofconstraints: 1)adversarial constraint to generate synthetic frames that aresimilar to the real ones, 2)cyclic consistency with the introduced perceptualloss for effective content conversion as well as style preservation, and3)similarity constraints across and within domains to enhance the content andmotion consistency in both spatial and temporal spaces at a fine-grained level.Furthermore, the current public available infrared and visible light datasetsare mainly used for object detection or tracking, and some are composed ofdiscontinuous images which are not suitable for video tasks. Thus, we provide anew dataset for I2V video translation, which is named IRVI. Specifically, ithas 12 consecutive video clips of vehicle and monitoring scenes, and bothinfrared and visible light videos could be apart into 24352 frames.Comprehensive experiments validate that I2V-GAN is superior to the comparedSOTA methods in the translation of I2V videos with higher fluency and finersemantic details. The code and IRVI dataset are available athttps://github.com/BIT-DA/I2V-GAN.

I2V-GAN: Unpaired Infrared-to-Visible Video Translation