Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

Generative Adversarial Networks (GANs) have achieved impressive results in various image
synthesis tasks, and are becoming a hot topic in computer vision research because of the …

Automatic 3D content creation has achieved rapid progress recently due to the availability of
pre-trained, large language models and image diffusion models, forming the emerging topic …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

As several industries are moving towards modeling massive 3D virtual worlds, the need for
content creation tools that can scale in terms of the quantity, quality, and diversity of 3D …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of
large-scale real-scanned 3D databases. To facilitate the development of 3D perception …

Abstract Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in
image-based 3D reconstruction. However, their implicit volumetric representations differ …

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …

We present an efficient method for joint optimization of topology, materials and lighting from
multi-view image observations. Unlike recent multi-view reconstruction approaches, which …