Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process is complex, the underlying sequential design process encodes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.

翻译：体量设计（又称体块设计）是专业建筑设计中首要且关键的步骤，本质上具有序列化特性。由于体量设计过程的复杂性，其底层的序列化设计流程蕴含着对设计师有价值的信息。现有研究致力于自动生成合理的体量设计方案，但所生成方案的质量参差不齐，而评估设计方案要么需要一套过于全面的指标集，要么依赖昂贵的人力专业知识。尽管以往方法仅关注学习最终设计而非序列化设计任务，本文提出从一组专家或高性能设计序列中编码设计知识，并利用基于Transformer的模型提取有效表征。随后，我们利用所学表征实现关键下游应用，例如设计偏好评估与程序化设计生成。通过估计所学表征的密度，我们开发了偏好模型；同时训练自回归Transformer模型用于序列化设计生成。我们借助包含数千个序列化体量设计的新数据集验证了上述想法。我们的偏好模型可比较任意给定的两个设计序列，在对抗随机设计序列的评估中准确率接近90%。此外，自回归模型能够基于部分设计序列自动补全完整的体量设计序列。