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Synthesizing Indoor Scene Layouts in Complicated Architecture Using Dynamic Convolution Networks

Synthesizing indoor scene layouts is challenging and critical, especially for digital design and gaming entertainment. Although there has been significant research on the indoor layout synthesis of rectangular-shaped or L-shaped architecture, there is little known about synthesizing plausible layouts for more complicated indoor architecture with both geometric and semantic information of indoor architecture being fully considered. In this paper, we propose an effective and novel framework to synthesize plausible indoor layouts in various and complicated architecture. The given indoor architecture is first encoded to our proposed representation, called InAiR, based on its geometric and semantic information. The indoor objects are grouped and then arranged by functional blocks, represented by oriented bounding boxes, using dynamic convolution networks based on their functionality and human activities. Through comparisons with other approaches as well as comparative user studies, we find that our generated indoor scene layouts for diverse, complicated indoor architecture are visually indistinguishable, which reach state-of-the-art performance.