Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.
Implementation issues represent an unfamiliar challenge to most control engineers, and many techniques for controller design ignore these issues outright. Consequently, the design of controllers for smart structural systems usually proceeds without regard for their eventual implementation, thus resulting either in serious performance degradation or in hardware requirements that squander power, complicate integration, and drive up cost. This paper summarizes previously reported results in the application of FPGAs to the realization of digital controllers for smart structures and reports ongoing effort to extend these results to multiple input, multiple output systems.
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