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

The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

Recently, DreamFusion demonstrated the utility of a pretrained text-to-image diffusion model
to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis …

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 …

In recent years, neural implicit surface reconstruction methods have become popular for
multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

In this work, we present a novel framework built to simplify 3D asset generation for amateur
users. To enable interactive generation, our method supports a variety of input modalities …

In this paper, we develop position embedding transformation (PETR) for multi-view 3D
object detection. PETR encodes the position information of 3D coordinates into image …

Modeling flexible macromolecules is one of the foremost challenges in single-particle
cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental …