Text-to-3D modelling has seen exciting progress by combining generative text-to-image
models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently …

Modeling the 3D world from sensor data for simulation is a scalable way of developing
testing and validation environments for robotic learning problems such as autonomous …

We present a complete system for real-time rendering of scenes with complex appearance
previously reserved for offline use. This is achieved with a combination of algorithmic and …

Unsupervised learning with functional data is an emerging paradigm of machine learning
research with applications to computer vision, climate modeling and physical systems. A …

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to
heat and particle flux constraints on the divertor target. Simulation is an important tool to …

The problem of inferring object shape from a single 2D image is underconstrained. Prior
knowledge about what objects are plausible can help, but even given such prior knowledge …

We ask whether 3D objects can be reconstructed from real world data collected for some
other purpose such as autonomous driving or augmented reality thus inferring objects only …

Abstract Implicit Neural Representations (INRs) have revolutionized signal representation by
leveraging neural networks to provide continuous and smooth representations of complex …

Reasoning over the interplay between object deformation and force transmission through
contact is central to the manipulation of compliant objects. In this paper, we propose Neural …

Recently, generative models for 3D objects are gaining much popularity in VR and
augmented reality applications. Training such models using standard 3D representations …