AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
This work addresses the demand for efficient 3D asset creation in professional development and user-generated content, presenting a flexible framework for modular 3D assets, though it appears incremental as it adapts existing language model techniques to a specific domain.
The paper tackles the problem of generating high-quality, diverse modular 3D assets from textual descriptions for digital industries like user-generated content, introducing AssetFormer, an autoregressive Transformer-based model that enhances asset generation quality through innovative module sequencing and decoding techniques.
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.