Regularized Adversarial Training For Single-Shot Virtual Try-On

Spatially placing an object onto a background is an essential operation in graphic design and facilitates many different applications such as virtual try-on. The placing operation is formulated as a geometric inference problem for given foreground and background images, and has been approached by spatial transformer architecture. In this paper, we propose a simple yet effective regularization technique to guide the geometric parameters based on user-defined trust regions. Our approach stabilizes the training process of spatial transformer networks and achieves a high-quality prediction with single-shot inference. Our proposed method is independent of initial parameters, and can easily incorporate various priors to prevent different types of trivial solutions. Empirical evaluation with the Abstract Scenes and CelebA datasets shows that our approach achieves favorable results compared to baselines.