Learning-based 3D reconstruction methods have shown impressive results. However, most
methods require 3D supervision which is often hard to obtain for real-world datasets …

The goal of this project is to learn a 3D shape representation that enables accurate surface
reconstruction, compact storage, efficient computation, consistency for similar shapes …

Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a
minor role in the deep learning revolution. Leading methods for learning generative models …

With the widespread usage of VR devices and contents, demands for 3D scene generation
techniques become more popular. Existing 3D scene generation models, however, limit the …

Any solid object can be decomposed into a collection of convex polytopes (in short,
convexes). When a small number of convexes are used, such a decomposition can be …

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of
a category and generating dense correspondences among shapes. With DIF, a 3D shape is …

Semantic reconstruction of indoor scenes refers to both scene understanding and object
reconstruction. Existing works either address one part of this problem or focus on …

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic
designs. But, while a vast body of work has focused on generative algorithms for raster …

We propose to represent shapes as the deformation and combination of learnt elementary
3D structures. We demonstrate this decomposition in learnt elementary 3D structures is …

We present a cascaded diffusion model based on a part-level implicit 3D representation. Our
model achieves state-of-the-art generation quality and also enables part-level shape editing …