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 …

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike
most existing models that learn to deterministically translate a latent vector to a shape, our …

We propose a 3D shape generation framework (SDF-Diffusion in short) that uses denoising
diffusion models with continuous 3D representation via signed distance fields (SDF). Unlike …

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 …

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 …

Diffusion models have shown great promise for image generation, beating GANs in terms of
generation diversity, with comparable image quality. However, their application to 3D …

We present a cascaded diffusion model based on a part-level implicit 3D representation. Our
model achieves state-of-the-art generation quality and also enables part-level shape editing …

With the rising industrial attention to 3D virtual modeling technology, generating novel 3D
content based on specified conditions (eg text) has become a hot issue. In this paper, we …

Abstract Recent Diffusion Transformers (ie, DiT) have demonstrated their powerful
effectiveness in generating high-quality 2D images. However, it is unclear how the …

3D data that contains rich geometry information of objects and scenes is valuable for
understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it …