This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

In this paper, we present a novel diffusion model called that generates multiview-consistent
images from a single-view image. Using pretrained large-scale 2D diffusion models, recent …

Diffusion models have emerged as the best approach for generative modeling of 2D images.
Part of their success is due to the possibility of training them on millions if not billions of …

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great
potential in novel view synthesis of a scene. However, current NeRF-based methods cannot …

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

Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity,
and various recent works have extended NeRF to handle dynamic scenes. A common …

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 …

Abstract We present Instant Volumetric Head Avatars (INSTA), a novel approach for
reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic …

Abstract Neural Radiance Fields (NeRF) have recently gained a surge of interest within the
computer vision community for its power to synthesize photorealistic novel views of real …