This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

Modelling interactions between humans and objects in natural environments is central to
many applications including gaming, virtual and mixed reality, as well as human behavior …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based
approach for precise and high-fidelity point cloud completion. Unlike existing point cloud …

Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This
paper presents a new point cloud upsampling network called PU-GAN, which is formulated …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

We propose a generative adversarial network for point cloud upsampling, which can not
only make the upsampled points evenly distributed on the underlying surface but also …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …