אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

StyleGAN is already quite famous for its unremarkable image editing capabilities. Although other generative models (e.g. diffusion models) achieve comparable synthesis quality, they cannot reproduce these semantically richmanipulations. In particular, StyleGAN allows the modification of various attributes, such as hair, age, pose, expression, and make-up, while still maintaining a high level of realism. Yet, it is still challenging to leverage these traits for real data or new domains. In this talk, we discuss three main obstacles: First, how to edit real images using latent-based manipulation, i.e., inverting a real image to StyleGAN'slatent space. Second, how to employ the per-image editing over videos, which requires another dimension of realism - temporal consistency. Lastly, we explore the possibilities of training StyleGAN in new and exciting domains. Short bio: I am a Computer Science Ph.D. student at Tel-Aviv University, under the supervision of Prof. Daniel Cohen-Or and Dr. Amit H. Bermano. Previously, I spent the 2020 summer at FAIR under the supervision of Prof. Lior Wolf, and the 2021 summer at Google Research. My main research interest is machine learning applications for computer vision and graphics. In particular, I work on image and video synthesis, while also interested in disentanglement, temporal coherence, supervision reduction, and the utilization of pre-trained models.