The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

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 …

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and
LAION have propelled recent dramatic progress in AI. Large neural models trained on such …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

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 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 …

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising
diffusion probabilistic models. While existing diffusion-based methods operate on images …

We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects.
This approach utilizes pretrained models from image captioning, image-text alignment, and …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

Powerful priors allow us to perform inference with insufficient information. In this paper, we
propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape …