Our next guest, Julian Chibane, is a PhD student at the Real Virtual Humans group at the Max Planck Institute for Informatics in Germany. His recent work centers around intrinsic functions for 3D reconstruction, and his most recent paper at NeurIPS is Neural Unsigned Distance Fields for Implicit Function Learning. He also introduced Implicit Feature Networks (IF-Nets) in Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion. Highlights 🖼 How, surprisingly, the IF-Net architecture learned reasonable representations of humans & objects without priors 🔢 A simple observation that led to Neural Unsigned Distance Fields, which handle 3D scenes without a clear inside vs. outside (most scenes!) 📚 Navigating open questions in 3D representation, and the importance of focusing on what's working