Feed-forward 3D generative models like the Large Reconstruction Model (LRM)[18] have
demonstrated exceptional generation speed. However, the transformer-based methods do …

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 …

Text-to-3D generation has shown rapid progress in recent days with the advent of score
distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural …

This paper introduces a novel approach to synthesize texture to dress up a 3D object, given
a text prompt. Based on the pre-trained text-to-image (T2I) diffusion model, existing methods …

Text-to-image diffusion models have recently emerged at the forefront of image generation,
powered by very large-scale unsupervised or weakly supervised text-to-image training …

We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields
(NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach …

Perceiving 3D structures from RGB images based on CAD model primitives can enable an
effective, efficient 3D object-based representation of scenes. However, current approaches …

Since diffusion models (DM) and the more recent Poisson flow generative models (PFGM)
are inspired by physical processes, it is reasonable to ask: Can physical processes offer …

In this paper, we present a novel shape reconstruction method leveraging diffusion model to
generate 3D sparse point cloud for the object captured in a single RGB image. Recent …

Parametric CAD models encode entire families of shapes that should, in principle, be easy
for designers to explore. However, in practice, parametric CAD models can be difficult to …