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

• URL: http://arxiv.org/abs/2312.09069v2
• Date: Sun, 21 Apr 2024 07:08:15 GMT
• Title: PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion
• Authors: Ying-Tian Liu, Yuan-Chen Guo, Guan Luo, Heyi Sun, Wei Yin, Song-Hai Zhang,
• Abstract summary: 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. 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.
• Score: 18.82883336156591
• License: http://creativecommons.org/licenses/by/4.0/
• 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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