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arXiv · 2312.09069

PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion

Ying-Tian Liu·Yuan-Chen Guo·Guan Luo·Heyi Sun·Wei Yin·Song-Hai Zhang
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Abstract

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, the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper, we present PI3D, a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly, we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.

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Ying-Tian Liu, Yuan-Chen Guo, Guan Luo, Heyi Sun, Wei Yin, Song-Hai Zhang. 2024-04-21. PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion. https://arxiv.org/abs/2312.09069

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