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 investigate an open research task of generating controllable 3D textured
shapes from the given textual descriptions. Previous works either require ground truth …

Text-guided 3D shape generation remains challenging due to the absence of large paired
text-shape data, the substantial semantic gap between these two modalities, and the …

Recent works have demonstrated that natural language can be used to generate and edit
3D shapes. However, these methods generate shapes with limited fidelity and diversity. We …

We are seeing a Cambrian explosion of 3D shape representations for use in machine
learning. Some representations seek high expressive power in capturing high-resolution …

We propose a method to create plausible geometric and texture style variations of 3D
objects in the quest to democratize 3D content creation. Given a pair of textured source and …

We present a novel alignment-before-generation approach to tackle the challenging task of
generating general 3D shapes based on 2D images or texts. Directly learning a conditional …

While image diffusion models have made significant progress in text-driven 3D content
creation they often fail to accurately capture the intended meaning of text prompts especially …

Diffusion models trained on large-scale text-image datasets have demonstrated a strong
capability of controllable high-quality image generation from arbitrary text prompts. However …

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 …