Recent years have brought remarkable progress in 3D vision problems like view synthesis and inverse rendering. Despite these advancements, substantial challenges remain in material and lighting decomposition, geometry estimation, and view synthesis—particularly when handling a wide range of materials. In this talk, I will outline a few of these problems and present solutions that combine the principled structure and efficiency of physics-based rendering with the strong priors encoded in generative image and video models.
Bio:
Dor Verbin is a research scientist at Google DeepMind in San Francisco, where he works on computer vision, computer graphics, and machine learning. He received his Ph.D. in computer science from Harvard University. Previously, he received a double B.Sc. in physics and in electrical engineering from Tel Aviv University, after which he worked as a researcher at Camerai, developing real-time computer vision algorithms for mobile devices. He received the Best Student Paper Honorable Mention award at CVPR 2022.
