Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …

Point clouds provide a compact and efficient representation of 3D shapes. While deep
neural networks have achieved impressive results on point cloud learning tasks, they …

Recent work has demonstrated that volumetric scene representations combined with
differentiable volume rendering can enable photo-realistic rendering for challenging scenes …

The field of single-view 3D shape reconstruction and generation using deep learning
techniques has seen rapid growth in the past five years. As the field is reaching a stage of …

Reconstructing 3D shape from 2D sketches has long been an open problem because the
sketches only provide very sparse and ambiguous information. In this paper, we use an …

Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead
of the geometry representations produced by state-of-the-art 3D reconstruction and …

Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
Such a function can encode a high quality surface with intricate details into the parameters …

This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape
represented as a point cloud into primitive patches. The key to deep primitive segmentation …