AssetFormer：基于自回归变换器的模块化三维资产生成框架

摘要

数字产业对高质量、多样化的模块化3D资产需求日益增长，尤其在用户生成内容~(UGC)领域。本文提出AssetFormer——一种基于自回归Transformer的模型，能够根据文本描述生成模块化3D资产。我们的先导研究利用了从在线平台收集的真实世界模块化资产。该模型通过创新性地借鉴语言模型的模块序列化与解码技术，采用自回归建模方式提升资产生成质量。初步结果表明，AssetFormer能有效简化专业开发和UGC场景下的资产创建流程。本工作提出的可扩展灵活框架适用于多种模块化3D资产类型，为3D内容生成领域的发展做出贡献。代码已开源：https://github.com/Advocate99/AssetFormer。

English

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.

AssetFormer：基于自回归变换器的模块化三维资产生成框架

AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer

摘要

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