Well-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently addressing whether people can comfortably walk, sit, or manipulate objects. To bridge this gap, we present a Behavior-Aware Anthropometric Scene Generation framework. Our approach leverages vision–language models (VLMs) to analyze object–behavior relationships, translating spatial requirements into parametric layout constraints adapted to user-specific anthropometric data. We conducted comparative studies with state-of-the-art models using geometric metrics and a user perception study (N=16). We further conducted in-depth human-scale studies (individuals, N=20; groups, N=18). The results showed improvements in task completion time, trajectory efficiency, and human-object manipulation space. This study contributes a framework that bridges VLM-based interaction reasoning with anthropometric constraints, validated through both technical metrics and real-scale human usability studies.

In 3D design, specifying design objectives and visualizing complex shapes through text alone proves to be a significant challenge. Although advancements in 3D GenAI have significantly enhanced part assembly and the creation of high-quality 3D designs, many systems still to dynamically generate and edit design elements based on the shape parameters. To bridge this gap, we propose GenPara, an interactive 3D design editing system that leverages text-conditional shape parameters of part-aware 3D designs and visualizes design space within the Exploration Map and Design Versioning Tree. Additionally, among the various shape parameters generated by LLM, the system extracts and provides design outcomes within the user's regions of interest based on Bayesian inference. A user study N = 16 revealed that \textit{GenPara} enhanced the comprehension and management of designers with text-conditional shape parameters, streamlining design exploration and concretization. This improvement boosted efficiency and creativity of the 3D design process.

This study proposes a system that automatically generates 3D modeling sequences for various 3D shapes. Existing 3D modeling systems impose a high cognitive load on users, making it particularly difficult for beginners to approach. To address this issue, we developed a system that applies a method for inferring and extracting modeling sequences from 3D shapes to generate Command Modeling and Design Graphs without the need for additional modeling data collection. For this purpose, we reconstructed geometric elements and their structural relationships using a domain-specific language, efficiently modeling shape repetitions and symmetries. The proposed system infers modeling sequences from completed 3D models and converts them into workflow graphs, providing richer and more detailed sequence data compared to existing datasets. As a result, users are expected to significantly improve design efficiency through intuitive modeling processes and command support.

Various modalities have emerged in the field of 3D generative AI (GenAI) to enhance design outcomes. While some designers find inspiration in prompts to guide their design options, others prefer sketching to embody creative visions. Nonetheless, the impact of the different modalities of 3D GenAI on the design process remains largely unexplored. This study examines the utilization of prompt- and sketch-guided modalities within the design process by conducting linkography and workflow analyses with 12 designers. The results revealed that prompts played a pivotal role in stimulating initial ideation, whereas sketches played a crucial role in embodying design ideas. This investigation highlights the distinct contributions of these modalities at different phases of the design process, suggesting the potential for a more refined and synergistic collaboration between humans and AI. By elucidating the diverse functions of sketches and prompts, we propose prospective directions for the UX framework of the 3D GenAI.

The Bayesian information gain (BIG) framework has garnered significant interest as an interaction method for predicting a user’s intended target based on a user’s input. However, the BIG framework is constrained to goal-oriented cases, which renders it difficult to support changing goal-oriented cases such as design exploration. During the design exploration process, the design direction is often undefined and may vary over time. The designer’s mental model specifying the design direction is sequentially updated through the information-retrieval process. Therefore, tracking the change point of a user’s goal is crucial for supporting an information exploration. We introduce the BIGexplore framework for changing goal-oriented cases. BIGexplore detects transitions in a user’s browsing behavior as well as the user’s next target. Furthermore, a user study on BIGexplore confirms that the computational cost is significantly reduced compared with the existing BIG framework, and it plausibly detects the point where the user changes goals.

C-Space is an interactive prototyping platform for collaborative spatial design exploration. Spatial design projects often begin with conceptualization that includes abstract diagramming, zoning, and massing to provide a foundation for making design decisions. Specifically, abstract diagrams guide designers to explore alternative designs without thinking prematurely about the details. However, complications arise when communicating ambiguous and incomplete designs to collaborators. To overcome this drawback, designers devote considerable amounts of time and resources into searching for design references and creating rough prototypes to explicate their design concepts better. Therefore, this study proposes C-Space, a novel design support system that integrates the abstract diagram with design reference retrieval and prototyping through a tangible user interface and augmented reality. Through a user study with 12 spatial designers, we verify that C-Space promotes rapid and robust spatial design exploration, inducing collaborative discussions and motivating users to interact with designs.

To enhance missing nonverbal cues in computer-mediated communication using text, those who can see often use emojis or emoticons. Although emojis for the sighted have transformed throughout the years to animated forms and added sound effects, emojis for visually impaired people remain underdeveloped. This study tested how tactile emojis based on visual imagery combined with the Braille system can enhance clarity in the computer-mediated communication environment for those with visual impairments. Results of this study confirmed three things: Visually impaired subjects were able to connect emotional emojis to the emotion they represented without any prior guidance, image-based (picture-based) and non-image-based (abstraction-based) tactile emoji were equally learnable, and the clarity of intended meaning was improved when an emoji was used with text (Braille). Thirty visually impaired subjects were able to match an average of 67% of emotions without prior guidance, and three of the four subjects who matched perfectly both before and after guidance were congenitally blind. The subjects had the most trouble discriminating the facial feature of “fear” between “sadness” or “surprised” for they shared similar traits. After guidance, the image-based tactile design elicited an average of 81% correct answers, whereas the non-image-based tactile design elicited an average of 37%, showing that the image-based tactile design was more effective for learning the meaning of emojis. The clarity of the sentence was also improved. This study shows that image-based tactile emojis can improve the texting experience of visually impaired individuals to a level where they can communicate subtle emotional cues through tactile imagery. This advance could minimize the service gap between sighted and visually impaired people and offer a much more abundant computer-mediated communication environment for visually impaired individuals.