论文标题
V-Dream：生成设计解决方案空间的沉浸式探索
V-Dream: Immersive Exploration of Generative Design Solution Space
论文作者
论文摘要
生成设计工作流程在计算设计领域引入了替代范式，使设计人员可以通过定义一组目标和约束来生成大量有效解决方案。但是，分析和缩小通常由各种高维特性组成的生成的解决方案空间一直是当前生成工作流程的主要挑战。通过利用交互式无限的空间探索以及虚拟现实平台中提供的视觉浸入，我们提出了V-Dream，这是一个虚拟现实生成分析框架，用于探索大规模的解决方案空间。 V-Dream提出了一个混合搜索工作流程，其中将空间随机搜索方法与推荐系统结合使用，允许用户选择所需的候选人并迭代地消除不希望的候选者。在每个周期中，V-Dream根据定义的特征在簇中重新组织剩余的选项。此外，我们的框架允许用户在各种层次级别上检查设计解决方案并评估其性能指标，从而帮助他们通过搜索/选择/重新聚类解决方案以沉浸式方式缩小解决方案空间。最后，我们介绍了我们提出的框架的原型，说明了用户如何从Autodesk的DreamCatcher软件生成的16000多个监视器支架中导航和缩小所需的解决方案。
Generative Design workflows have introduced alternative paradigms in the domain of computational design, allowing designers to generate large pools of valid solutions by defining a set of goals and constraints. However, analyzing and narrowing down the generated solution space, which usually consists of various high-dimensional properties, has been a major challenge in current generative workflows. By taking advantage of the interactive unbounded spatial exploration, and the visual immersion offered in virtual reality platforms, we propose V-Dream, a virtual reality generative analysis framework for exploring large-scale solution spaces. V-Dream proposes a hybrid search workflow in which a spatial stochastic search approach is combined with a recommender system allowing users to pick desired candidates and eliminate the undesired ones iteratively. In each cycle, V-Dream reorganizes the remaining options in clusters based on the defined features. Moreover, our framework allows users to inspect design solutions and evaluate their performance metrics in various hierarchical levels, assisting them in narrowing down the solution space through iterative cycles of search/select/re-clustering of the solutions in an immersive fashion. Finally, we present a prototype of our proposed framework, illustrating how users can navigate and narrow down desired solutions from a pool of over 16000 monitor stands generated by Autodesk's Dreamcatcher software.
