Angela Dai

  • Affiliation: Technical University of Munich
  • Web Site:
  • Email: angela.dai [at]
  • Title: Learned Parametric 3D Shape Models
  • Date & room: Tuesday, 4th July at 14:00 - 15:00 (Rooms: 1A+1B)


Recent years have seen strong advances in learning general priors for 3D scene understanding tasks, while the burgeoning rise of interest in neural scene representations has reinvigorated per-scene optimization paradigms. In this talk, we discuss generative 3D tasks such as 3D reconstruction from the perspective of traditional optimization techniques and the incorporation of learned priors, along with how to effectively combine to two together. We start with 3D scene reconstruction optimization, followed by leveraging learned priors to infer unobserved information, performing geometric completion in 3D scans. We then explore learning a 3D texture manifold as a prior for test-time optimization to support texturing from arbitrary RGB image queries, where the learned prior serves as regularization across incongruous geometry and pose. Next, we introduce a new paradigm to learn a shape manifold from optimized neural fields that enables a dimension-agnostic approach for high-dimensional generative modeling. Finally, we explore how to learn 3D priors for scenes and how to enable new outlooks on 3D scene generative modeling.


Angela Dai is an Assistant Professor at the Technical University of Munich where she leads the 3D AI group. Prof. Dai's research focuses on understanding how the 3D world around us can be modeled and semantically understood. Previously, she received her PhD in computer science from Stanford in 2018 and her BSE in computer science from Princeton in 2013. Her research has been recognized through a Eurographics Young Researcher Award, Google Research Scholar Award, ZDB Junior Research Group Award, an ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention, as well as a Stanford Graduate Fellowship