DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding

• URL: http://arxiv.org/abs/2312.02192v2
• Date: Wed, 17 Jul 2024 17:03:58 GMT
• Title: DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding
• Authors: Uy Dieu Tran, Minh Luu, Phong Ha Nguyen, Khoi Nguyen, Binh-Son Hua,
• Abstract summary: Existing text-to-3D methods tend to have mode collapses, and hence poor diversity in their results. We propose a new method that considers the joint generation of different 3D models from the same text prompt. We show that our method leads to improved diversity in text-to-3D synthesis qualitatively and quantitatively.
• Score: 15.341857735842954
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors. An intriguing but underexplored problem with existing text-to-3D methods is that 3D models obtained from the sampling-by-optimization procedure tend to have mode collapses, and hence poor diversity in their results. In this paper, we provide an analysis and identify potential causes of such a limited diversity, which motivates us to devise a new method that considers the joint generation of different 3D models from the same text prompt. We propose to use augmented text prompts via textual inversion of reference images to diversify the joint generation. We show that our method leads to improved diversity in text-to-3D synthesis qualitatively and quantitatively. Project page: https://diversedream.github.io

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