We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for
generative diffusion models. Our shape representation can encode 3D shapes given as …

We present a StyleGAN2‐based deep learning approach for 3D shape generation, called
SDF‐StyleGAN, with the aim of reducing visual and geometric dissimilarity between …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

We propose a new representation for encoding 3D shapes as neural fields. The
representation is designed to be compatible with the transformer architecture and to benefit …

Generative models aim to learn the distribution of observed data by generating new
instances. With the advent of neural networks, deep generative models, including variational …

Masked Autoencoders (MAE) have shown promising performance in self-supervised
learning for both 2D and 3D computer vision. However, existing MAE-style methods can only …

Latent space EBMs, also known as energy-based priors, have drawn growing interests in
the field of generative modeling due to its flexibility in the formulation and strong modeling …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

We propose a generative model of unordered point sets, such as point clouds, in the forms
of an energy-based model, where the energy function is parameterized by an input …

This paper studies the cooperative learning of two generative flow models, in which the two
models are iteratively updated based on the jointly synthesized examples. The first flow …