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The paper introduces AssetFormer, an autoregressive Transformer model for generating modular 3D assets from text descriptions by sequencing and decoding primitive modules. Addressing the need for high-quality, diverse 3D assets, especially for UGC, AssetFormer generates assets composed of primitives that adhere to constrained design parameters. Experiments demonstrate the model's effectiveness in streamlining asset creation, suggesting its potential for professional development and UGC scenarios.
Autoregressive Transformers can now generate modular 3D assets from text, opening new avenues for UGC and professional 3D content creation.
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.
