Nov 11, 2022 · Here, we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and …

Dec 2, 2024 · In order to fill the gap in 2D-3D multi-modal joint generation for autonomous driving, in this paper, we propose our framework, \emph {HoloDrive}, to jointly generate the camera …

A collection of resources on 2D Generative Model which utilize generator functions that map low-dimensional latent codes to high-dimensional data outputs..

Mar 26, 2025 · Here, we introduce a symmetry-constrained diffusion model (SCDM) that integrates space group symmetry into the generative process. By incorporating Wyckoff …

This thesis focuses on addressing such real-world constraints in 2D and 3D generative models. Firstly, we focus on improving the data efficiency of class-conditional Generative Adversarial …

Herein, a generative material design pipeline known as the material transformer generator (MTG) is proposed. MTG leverages two distinct 2D material composition generators, both trained …

We present a novel apporach for text to 3D generation by repurposing well pre-trained 2D diffusion models. Our approach is motivated by the fact that high-quality 3D data is …

Dec 4, 2024 · Today we introduce Genie 2, a foundation world model capable of generating an endless variety of action-controllable, playable 3D environments for training and evaluating …

In this practical, we will investigate the fundamentals of generative modelling – a machine learning framework that allows us to learn how to sample new unseen data points that match the...

In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses …