Learning to control visual data translation - Koksal, Ali (Nanyang Technological University, 2023)
With the advancements in deep learning models such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the generation of high-dimensional data such as images, videos, etc. has achieved photo-realistic results with image-to-image translation advances. In particular, the ability to control the translation is an important aspect of modifying and synthesis novel contents. In this thesis, we address controllable high-dimensional data translation that enables three variations: (i) unpaired image-to-image translation, (ii) motion controllable video generation, and (iii) motion-aware mask-to-frame translation.