GAMES Webinar 2018-64期（Siggraph 2018优秀博士论文报告）| 朱俊彦（麻省理工学院）
【GAMES Webinar 2018-64期（Siggraph 2018优秀博士论文报告）】
报告时间：2018年9月13日（星期四）晚8:00 – 9:30（北京时间）
报告题目：Learning to Generate Images
Humans are avid consumers of visual content. Every day, people watch videos, play digital games, and share photos on social media. However, there is an asymmetry — while everybody can consume visual data, only a chosen few are talented enough to effectively express themselves visually. For the rest of us, most attempts at creating or manipulating realistic visual content end up quickly “falling off” the manifold of natural images. In this talk, we investigate a few data-driven approaches for preserving visual realism while creating and manipulating photographs. We use these methods as training wheels for visual content creation. We first propose to model visual realism directly from large-scale natural images. We then define a class of image synthesis and manipulation operations, constraining their outputs to look realistic according to the learned models. The presented methods not only help users easily synthesize more visually appealing photos but also enable new effects not possible before this work. This talk contains three parts. Part I describes discriminative methods for modeling the photorealism and photograph aesthetics. Part II presents approaches that directly model the natural image manifold via generative models and constrain the output of a photo editing tool to lie on this manifold. Part III combines the discriminative learning and generative modeling into an end-to-end image-to-image translation framework, where a network is trained to map inputs (such as user sketches) directly to natural looking results.
Jun-Yan Zhu is a postdoctoral researcher at MIT CSAIL. He obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2017 after spending five years at CMU and UC Berkeley. He received his B.E in Computer Sciences from Tsinghua University in 2012. His research interests are in computer vision, computer graphics, and machine learning, with the goal of building machines capable of understanding and recreating our visual world. His Ph.D. work was supported by a Facebook Fellowship. His dissertation won the 2018 ACM SIGGRAPH Outstanding Doctoral Dissertation Award from SIGGRAPH and 2017-18 David J. Sakrison Memorial Prize for outstanding doctoral research from the UC Berkeley EECS Department. He served as a Technical Paper Committee member at SIGGRAPH Asia 2018.
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