We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from
sparse-view images in around 0.1 s. GRM is a feed-forward transformer-based model that …

Recent advancements in 3D reconstruction from single images have been driven by the
evolution of generative models. Prominent among these are methods based on Score …

This paper aims to tackle the problem of modeling dynamic urban streets for autonomous
driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to …

We propose\textbf {DMV3D}, a novel 3D generation approach that uses a transformer-based
3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model …

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to
powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these …

Various techniques have been developed and introduced to address the pressing need to
create three-dimensional (3D) content for advanced applications such as virtual reality and …

Generalist robot manipulators need to learn a wide variety of manipulation skills across
diverse environments. Current robot training pipelines rely on humans to provide kinesthetic …

We present a method for generating Streetscapes—long sequences of views through an on-
the-fly synthesized city-scale scene. Our generation is conditioned by language input (eg …

We propose NViST a transformer-based model for efficient and generalizable novel-view
synthesis from a single image for real-world scenes. In contrast to many methods that are …

This work offers a discussion on how computational mechanics and physics-informed
machine learning can be integrated into the process of sensing, understanding, and …