House-GAN++: Generative Adversarial Layout Refinement Networks (2103.02574v1)

Abstract: This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator also creates a new opportunity in further improving a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative layout refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects.

House-GAN++: Generative Adversarial Layout Refinement Networks

The paper "House-GAN++: Generative Adversarial Layout Refinement Networks" introduces a novel neural network architecture designed for the automated generation and refinement of residential floorplans. This architecture represents a significant advancement in the computational design domain, leveraging Generative Adversarial Networks (GANs) to iteratively refine floorplans and tackle design complexities typically handled by professional architects.

Core Contributions

The paper makes several notable contributions to the area of automated layout generation:

1. Integration of GAN Variants: The proposed architecture combines a graph-constrained relational GAN with a conditional GAN. This integration allows the previously generated layout to serve as a constraint for the subsequent refinements, thus enabling a more accurate and iterative design process.
2. Component-wise GT-conditioning: A key insight from their research is that a non-iterative training process, known as component-wise GT-conditioning, proves effective for learning an iterative generator. By selectively passing ground-truth conditions to components such as rooms and doors, the approach skillfully balances between design completion and refinement.
3. Advanced Layout Capabilities: The system is capable of handling non-rectangular rooms and incorporating functional elements such as doors and entrances, moving closer to practical applications in intelligent computational agents for architectural design.

Technical Innovations

The architecture retains the convolutional message passing framework from the state-of-the-art House-GAN but enhances it with three primary technical innovations:

• Edge Features: The system encodes doors and functional connections via edges in the graph, enhancing the fidelity of the generated layouts.
• Conditional Masking: Each node and edge can take a 2D segmentation mask as input, which assists in maintaining or refining specific design elements during the generation process.
• Meta-Optimization: A meta-algorithm is employed at test time to refine the iterative process further, optimizing layout generation based on target metrics such as diversity and compatibility.

Evaluation and Results

The authors conducted extensive evaluations using the RPLAN dataset, which contains about 60,000 vector-graphics floorplans. The proposed system was assessed using three standard metrics: realism, diversity, and compatibility, outperforming existing methods by significant margins. Notably, the system's output is competitive against ground-truth layouts, designed by professional architects.

Quantitative evaluations revealed substantial improvements over prior approaches, particularly in complex tasks involving layouts with a higher number of rooms. The system demonstrated advanced realism and diversity, particularly through its refinement capabilities, which are ideally suited for practical applications where iterative feedback loops are essential.

Implications and Future Work

The implications of this research extend to both practical and theoretical domains. Practically, the system offers a tool that could reduce the cost and time associated with generating professional-grade architectural designs, potentially impacting the construction and real-estate industries. Theoretically, this work contributes to the understanding of how complex design tasks can be tackled by integrating iterative refinements within GAN frameworks.

The findings suggest future research avenues, such as exploring further optimization of the refinement strategies and incorporating more sophisticated constraints, such as those related to environmental sustainability or cost-effectiveness. Additionally, adapting this framework for other types of layout design tasks, beyond residential floorplans, could broaden its applicability and validate its versatility.

In conclusion, "House-GAN++" sets a new precedent in automated architectural design, illustrating how advanced neural network architectures can be harnessed to mimic and augment the creative processes traditionally performed by human experts.