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 …

Generative systems for graphical assets have the potential to provide users with high quality
assets at the push of a button. However, there are many forms of assets, and many …

Abstract We present Neural Shape Deformation Priors, a novel method for shape
manipulation that predicts mesh deformations of non-rigid objects from user-provided …

Abstract We propose Neural 3D Articulated object Prior (NAP), the first 3D deep generative
model to synthesize 3D articulated object models. Despite the extensive research on …

This paper presents a new approach for 3D shape generation, inversion, and manipulation,
through a direct generative modeling on a continuous implicit representation in wavelet …

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural
abnormalities, often requiring customized treatment plans for individual patients …

This paper introduces a new approach based on a coupled representation and a neural
volume optimization to implicitly perform 3D shape editing in latent space. This work has …

Learning implicit templates as neural fields has recently shown impressive performance in
unsupervised shape correspondence. Despite the success, we observe current approaches …

In deformable object manipulation, we often want to interact with specific segments of an
object that are only defined in non-deformed models of the object. We thus require a system …

In the realm of 3D computer vision, parametric models have emerged as a ground-breaking
methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely …