AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
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.
699d4d754e37ec6dfa1bc6c8
Lingting Zhu
699d4d754e37ec6dfa1bc6c9
Shengju Qian
claimed_verified
2026-02-25T17:31:35.899Z
6380580f42cedbc20c7bef71
/avatars/8d710e0de551cd2bf545cc31fcaf099d.svg
Shengju Qian
thesouthfrog
user
thesouthfrog
699d4d754e37ec6dfa1bc6ca
Haidi Fan
699d4d754e37ec6dfa1bc6cb
Jiayu Dong
699d4d754e37ec6dfa1bc6cc
Zhenchao Jin
699d4d754e37ec6dfa1bc6cd
Siwei Zhou
699d4d754e37ec6dfa1bc6ce
Gen Dong
699d4d754e37ec6dfa1bc6cf
Xin Wang
699d4d754e37ec6dfa1bc6d0
Lequan Yu
AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
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 is an autoregressive Transformer-based model that generates modular 3D assets from textual descriptions by adapting language model techniques to handle design constraints.
Transformer-based model autoregressive modeling modular 3D assets textual descriptions module sequencing decoding techniques