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

TL;DR

This paper introduces a visual context-aware graph network that learns global relations to recommend optimal building locations in in-game residential layouts, improving spatial coherence and customization.

Contribution

It presents a novel global relation learning algorithm combining scene graphs and depth images for accurate building placement in virtual environments.

Findings

The method accurately predicts building locations reflecting spatial rules.

It outperforms baseline models in location recommendation accuracy.

The approach effectively integrates geometry semantics for scene understanding.

Abstract

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.