The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content~(UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation. The code is available at https://github.com/Advocate99/AssetFormer.

翻译：数字产业对高质量、多样化的模块化三维资产需求日益增长，尤其是在用户生成内容（UGC）领域。本研究提出AssetFormer，一种基于自回归Transformer的模型，旨在根据文本描述生成模块化三维资产。我们的初步研究利用了从在线平台收集的真实世界模块化资产数据集。AssetFormer致力于解决以下挑战：生成由基本构件组成的资产，这些构件需满足不同应用场景下的约束性设计参数。通过创新性地借鉴语言模型的模块序列化与解码技术，我们的方法通过自回归建模提升了资产生成质量。初步实验结果表明，AssetFormer在专业开发和UGC场景中能有效简化资产创建流程。本工作提出了一个可扩展至各类模块化三维资产的灵活框架，为三维内容生成领域的拓展作出贡献。代码发布于https://github.com/Advocate99/AssetFormer。