报告摘要: Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution of a generative model from a prior distribution of the latent space with the empirical ambient distribution of training instances. While this paradigm has proven to be quite successful on images, its current applications in 3D generation encounter fundamental challenges in the limited training data and generalization behavior. The key difference between image generation and shape generation is that 3D shapes possess various priors in geometry, topology, and physical properties. Existing probabilistic 3D generative approaches do not preserve these desired properties, resulting in synthesized shapes with various types of distortions. In this talk, I will discuss recent work that seeks to establish a novel geometric framework for learning shape generators. The key idea is to model various geometric, physical, and topological priors of 3D shapes as suitable regularization losses by developing computational tools in differential geometry and computational topology. We will discuss the applications in deformable shape generation, latent space design, joint shape matching, and 3D man-made shape generation.

生成模型将潜在参数空间映射到环境空间中的实例，在 3D 视觉和相关领域中有着广泛的应用。这些模型的标准方案是概率模型，它将生成模型从潜在空间的先验分布中诱导的环境分布与训练实例的经验环境分布对齐。虽然这种范式已被证明在图像上非常成功，但它在 3D 生成中的当前应用在有限的训练数据和泛化行为方面遇到了根本挑战。图像生成和形状生成之间的主要区别在于 3D 形状在几何、拓扑和物理属性方面具有各种先验。现有的概率 3D 生成方法没有保留这些所需的属性，导致合成形状具有各种类型的扭曲。在本次演讲中，我将讨论最近的工作，旨在建立一个用于学习形状生成器的新型几何框架。关键思想是通过开发微分几何和计算拓扑中的计算工具，将 3D 形状的各种几何、物理和拓扑先验建模为合适的正则化损失。我们将讨论可变形形状生成、潜在空间设计、关节形状匹配和 3D 人造形状生成中的应用。

报告人简介: Qixing Huang is an associate professor with tenure at the computer science department of the University of Texas at Austin. His research sits at the intersection of graphics, geometry, optimization, vision, and machine learning. He has published more than 100 papers at leading venues across these areas. His research has received several awards, including multiple best paper awards, the best dataset award at Symposium on Geometry Processing 2018, IJCAI 2019 early career spotlight, multiple industrial and NSF awards, and 2021 NSF Career award. He has also served as (senior) area chairs of ICLR, NeurIPS, ICML, CVPR, ECCV, ICCV and technical papers committees of SIGGRAPH and SIGGRAPH Asia, and co-chaired Symposium on Geometry Processing 2020.

黄其兴,德克萨斯大学奥斯汀分校计算机科学系的终身副教授。研究涉及图形、几何、优化、视觉和机器学习的交叉领域。在国际主流会议上发表了 100 多篇论文。研究获得了多个奖项，包括多个最佳论文奖、2018 年几何处理研讨会最佳数据集奖、IJCAI 2019 早期职业亮点奖、多个工业和 NSF 奖以及 2021 年 NSF 职业奖。曾担任 ICLR、NeurIPS、ICML、CVPR、ECCV、ICCV 和 SIGGRAPH 和 SIGGRAPH Asia 技术论文委员会的（高级）领域主席，并共同主持了 2020 年几何处理研讨会。

邀请人：李瑞辉

联系人：王萌