特邀报告4：Deep Learning for Graphics - A personal perspective
报告摘要：Starting from the success of AlexNet in 2012, deep neural networks have taken over the field of computer vision by storm. In contrast, the adoption of deep learning machinery by the Computer Graphics community has been much slower. Nevertheless, today deep networks can be found in all the major areas of Computer Graphics, including first and foremost image and video processing, but also animation, rendering, and 3D modeling.
In this talk, I will give a brief overview of my personal perspective of this trend, focusing on several exciting advances ranging from texture and image synthesis to motion style transfer for articulated characters. All of these results are enabled by the use of deep networks, demonstrating their potential for graphics, which the community has only begun to explore.
嘉宾介绍：Dani Lischinski is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. He received his PhD from Cornell University in 1994, and was a post-doctoral research associate at the University of Washington until 1996. In 2002/3, he spent a sabbatical year at Pixar Animation Studios. In 2012 he received the Eurographics Outstanding Technical Contributions Award. In 2017, he served as the Technical Papers Chair for SIGGRAPH Asia 2017. His areas of interest span a wide variety of topics in the fields of computer graphics, image and video processing, and computer vision. Most of his recent work involves deep neural networks and their applications in graphics and vision.