My Bitter Lesson with Computer Graphics

Abstract: In his essay The Bitter Lesson, Richard Sutton argued that general methods leveraging computation ultimately outperform hand-crafted ones. In this talk, I share my own version of this lesson, learned the hard way over a decade in computer graphics. Where does the bitter lesson apply to graphics, and where does it not? I argue that the answer depends on whether a problem genuinely requires 3D, physics, and control, or if it simply produces 2D pixels. I will also share a few advices for graduate students starting their research today, showing which directions will compound in value over a decade, and which will be quietly subsumed by the next scale-up. Bio: Giljoo Nam is a research scientist at Meta focused on Physical AI, building systems that understand the structure and behavior of the 3D physical world. His research bridges computer vision and graphics, with focus areas including generative AI, 3D reconstruction, motion tracking, inverse rendering, computational imaging, and human modeling. He earned his Ph.D. in Computer Science from KAIST in 2019.. Homepage: https://giljoonam.github.io/ Sponsor: The VASC seminar is generously sponsored by HeyGen, an all-in-one AI-powered video generation platform that leverages advances in computer vision, generative modeling, and multimodal learning to make high-quality video creation both scalable and accessible.