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.

翻译：我们提出分摊式文本到网格（AToM），一种同时优化多个文本提示的前馈式文本到网格框架。与现有的文本到3D方法（常需耗时的逐提示优化，且输出通常为非多边形网格表示）不同，AToM在不到1秒内直接生成高质量带纹理网格，训练成本降低约10倍，并能泛化至未见提示。我们的核心思想是新颖的三平面文本到网格架构，结合两阶段分摊式优化策略，确保训练稳定并支持可扩展性。通过在各类提示基准上的广泛实验，AToM显著优于最先进的分摊式方法，准确率（在DF415数据集中）提升4倍以上，并生成更可区分、更高质量的3D输出。AToM展现出强泛化能力，能在推理时无需进一步优化即为未见插值提示生成精细3D资产，与逐提示解决方案形成对比。