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 …

Abstract 3D content creation has achieved significant progress in terms of both quality and
speed. Although current feed-forward models can produce 3D objects in seconds, their …

We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D
generative models which produce a single output representation, Shap-E directly generates …

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 …

Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of
the trade-off between quality and efficiency, eg, MipNeRF presents fine-detailed and anti …

We introduce GaussianAvatars a new method to create photorealistic head avatars that are
fully controllable in terms of expression pose and viewpoint. The core idea is a dynamic 3D …

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

We present a technique for zero-shot generation of a 3D model using only a target text
prompt. Without any 3D supervision our method deforms the control shape of a limit …