AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matching
Zhongliang Tang, Qingrong Cheng, Mengchen Tan, Yongxiang Zhang, Fei Xia

TL;DR
AutoGameUI is an automated system that uses multimodal learning to efficiently generate high-fidelity game user interfaces by matching UI and UX designs, significantly reducing development time.
Contribution
The paper introduces a novel two-stage multimodal correspondence matching pipeline and a new GAMEUI dataset for automatic GameUI construction.
Findings
Achieves 3x faster UI development in mobile games.
Effectively maintains design consistency in constructed GameUI.
Validated on GAMEUI and RICO datasets with strong results.
Abstract
Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches and inefficiencies. To address the issue, we present an automatic system, AutoGameUI, for efficiently and accurately constructing GameUI. The system centers on a two-stage multimodal learning pipeline to obtain the optimal correspondences between UI and UX designs. The first stage learns the comprehensive representations of UI and UX designs from multimodal perspectives. The second stage incorporates grouped cross-attention modules with constrained integer programming to estimate the optimal correspondences through top-down hierarchical matching. The optimal…
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Taxonomy
TopicsInnovative Teaching and Learning Methods · Wikis in Education and Collaboration · Educational Games and Gamification
