Jun Gao University of Toronto
时间： 2023-04-18 16:00-2023-04-18 18:00
地点：C19-2 or 腾讯会议：https://meeting.tencent.com/dm/8ox32Vj3YvX7
With the increasing demand for creating large-scale 3D virtual worlds in many industries, there is an immense need for diverse and high-quality 3D content. Machine learning is existentially enabling this quest. In this talk, I will discuss how looking from the perspective of combining differentiable iso-surfacing with differentiable rendering could enable 3D content creation at scale and make a real-world impact. Towards this end, we first introduce a differentiable 3D representation based on a tetrahedral grid to enable high-quality recovery of 3D mesh with arbitrary topology. By incorporating differentiable rendering, we further design a generative model capable of producing 3D shapes with complex textures and materials for mesh generation. Our framework further paves the way for innovative high-quality 3D mesh creation from text prompt leveraging 2D diffusion models, which democratizes 3D content creation for novice users.
Jun Gao is a PhD student at the University of Toronto advised by Prof. Sanja Fidler. He also holds the position of Research Scientist at NVIDIA Toronto AI lab. His research interests focus on the intersection of 3D computer vision and computer graphics, particularly developing machine learning tools to facilitate large-scale 3D content creation and drive real-world applications. His work has been presented at prestigious conferences such as NeurIPS, CVPR, ICCV, ECCV, ICLR and SIGGRAPH. Many of his contributions have been implemented in products, including NVIDIA Picasso, GANVerse3D, Neural DriveSim and Toronto Annotation Suite. He will serve as an Area Chair at NeurIPS 2023.