Adversarial Model for Rotated Indoor Scenes Planning
This work addresses a specific challenge in interior scene synthesis for applications like design automation, but it is incremental as it builds on prior adversarial methods with targeted enhancements.
The paper tackles the problem of generating furniture layouts for rotated indoor scenes by proposing an adversarial model with three modules, which reduces mode collapse and improves layout quality across four room types.
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated. The proposed model combines a conditional adversarial network, a rotation module, a mode module, and a rotation discriminator module. As compared with the prior work on scene synthesis, our proposed three modules enhance the ability of auto-layout generation and reduce the mode collapse during the rotation of the interior room. We conduct our experiments on a proposed real-world interior layout dataset that contains 14400 designs from the professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts for four types of rooms, including the bedroom, the bathroom, the study room, and the tatami room.