AToM: Amortized Text-to-Mesh using 2D Diffusion

• URL: http://arxiv.org/abs/2402.00867v1
• Date: Thu, 1 Feb 2024 18:59:56 GMT
• Title: AToM: Amortized Text-to-Mesh using 2D Diffusion
• Authors: Guocheng Qian, Junli Cao, Aliaksandr Siarohin, Yash Kant, Chaoyang Wang, Michael Vasilkovsky, Hsin-Ying Lee, Yuwei Fang, Ivan Skorokhodov, Peiye Zhuang, Igor Gilitschenski, Jian Ren, Bernard Ghanem, Kfir Aberman, Sergey Tulyakov
• Abstract summary: Amortized Text-to-Mesh (AToM) is a feed-forward framework optimized across multiple text prompts simultaneously. AToM directly generates high-quality textured meshes in less than 1 second with around 10 times reduction in the training cost. AToM significantly outperforms state-of-the-art amortized approaches with over 4 times higher accuracy.
• Score: 107.02696990299032
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly output representations other than polygonal meshes, AToM directly generates high-quality textured meshes in less than 1 second with around 10 times reduction in the training cost, and generalizes to unseen prompts. Our key idea is a novel triplane-based text-to-mesh architecture with a two-stage amortized optimization strategy that ensures stable training and enables scalability. Through extensive experiments on various prompt benchmarks, AToM significantly outperforms state-of-the-art amortized approaches with over 4 times higher accuracy (in DF415 dataset) and produces more distinguishable and higher-quality 3D outputs. AToM demonstrates strong generalizability, offering finegrained 3D assets for unseen interpolated prompts without further optimization during inference, unlike per-prompt solutions.

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