In-game Residential Home Planning via Visual Context-aware Global Relation Learning


  • Lijuan Liu NetEase Fuxi AI Lab
  • Yin Yang Clemson University
  • Yi Yuan NetEase Fuxi AI Lab
  • Tianjia Shao Zhejiang University
  • He Wang Leeds University
  • Kun Zhou Zhejiang University





In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. Given a construction layout, we propose a visual context-aware graph generation network that learns the implicit global relations among the scene components and infers the location of a new building unit. The proposed network takes as input the scene graph and the corresponding top-view depth image. It provides the location recommendations for a newly added building units by learning an auto-regressive edge distribution conditioned on existing scenes. We also introduce a global graph-image matching loss to enhance the awareness of essential geometry semantics of the site. Qualitative and quantitative experiments demonstrate that the recommended location well reflects the implicit spatial rules of components in the residential estates, and it is instructive and practical to locate the building units in the 3D scene of the complex construction.




How to Cite

Liu, L., Yang, Y., Yuan, Y., Shao, T., Wang, H., & Zhou, K. (2021). In-game Residential Home Planning via Visual Context-aware Global Relation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 336-343.



AAAI Technical Track on Application Domains