Progressive Text-to-3D Generation for Automatic 3D Prototyping

• URL: http://arxiv.org/abs/2309.14600v1
• Date: Tue, 26 Sep 2023 01:08:35 GMT
• Title: Progressive Text-to-3D Generation for Automatic 3D Prototyping
• Authors: Han Yi, Zhedong Zheng, Xiangyu Xu and Tat-seng Chua
• Abstract summary: We propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. Our experiment verifies that the proposed method performs favorably against existing methods. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions.
• Score: 83.33407603057618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-3D generation is to craft a 3D object according to a natural language description. This can significantly reduce the workload for manually designing 3D models and provide a more natural way of interaction for users. However, this problem remains challenging in recovering the fine-grained details effectively and optimizing a large-size 3D output efficiently. Inspired by the success of progressive learning, we propose a Multi-Scale Triplane Network (MTN) and a new progressive learning strategy. As the name implies, the Multi-Scale Triplane Network consists of four triplanes transitioning from low to high resolution. The low-resolution triplane could serve as an initial shape for the high-resolution ones, easing the optimization difficulty. To further enable the fine-grained details, we also introduce the progressive learning strategy, which explicitly demands the network to shift its focus of attention from simple coarse-grained patterns to difficult fine-grained patterns. Our experiment verifies that the proposed method performs favorably against existing methods. For even the most challenging descriptions, where most existing methods struggle to produce a viable shape, our proposed method consistently delivers. We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions.

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