Reconstructing Static Scenes and Dynamic Humans with Implicit Neural Representations

3D reconstruction is a long-standing problem in computer vision and has a variety of applications such as virtual reality, 3D content generation, and telepresence. In this talk, I will present our progress on 3D reconstruction of static scenes and dynamic humans with implicit neural representations. The first part of the talk introduces an effective regularization when optimizing implicit neural representations on indoor scenes based on the Manhattan-world Assumption. In the second part, I will show some animatable implicit neural representations for modeling dynamic humans from videos.