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In this paper, we propose an effective global relation learning algorithm torecommend an appropriate location of a building unit for in-game customizationof residential home complex. Given a construction layout, we propose a visualcontext-aware graph generation network that learns the implicit globalrelations among the scene components and infers the location of a new buildingunit. The proposed network takes as input the scene graph and the correspondingtop-view depth image. It provides the location recommendations for anewly-added building units by learning an auto-regressive edge distributionconditioned on existing scenes. We also introduce a global graph-image matchingloss to enhance the awareness of essential geometry semantics of the site.Qualitative and quantitative experiments demonstrate that the recommendedlocation well reflects the implicit spatial rules of components in theresidential estates, and it is instructive and practical to locate the buildingunits in the 3D scene of the complex construction.

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