Alexander Htet Kyaw

Alexander Htet Kyaw, Miana Smith, Se Hwan Jeon et al.

We present a system that transforms speech into physical objects using 3D generative AI and discrete robotic assembly. By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. While generative AI models can produce a wide range of 3D meshes, AI-generated meshes are not directly suitable for robotic assembly or account for fabrication constraints. To address this, we contribute a workflow that integrates natural language, 3D generative AI, geometric processing, and discrete robotic assembly. The system discretizes the AI-generated geometry and modifies it to meet fabrication constraints such as component count, overhangs, and connectivity to ensure feasible physical assembly. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a robotic arm.

Alexander Htet Kyaw, Lenin Ravindranath Sivalingam

We present a node-based storytelling system for multimodal content generation. The system represents stories as graphs of nodes that can be expanded, edited, and iteratively refined through direct user edits and natural-language prompts. Each node can integrate text, images, audio, and video, allowing creators to compose multimodal narratives. A task selection agent routes between specialized generative tasks that handle story generation, node structure reasoning, node diagram formatting, and context generation. The interface supports targeted editing of individual nodes, automatic branching for parallel storylines, and node-based iterative refinement. Our results demonstrate that node-based editing supports control over narrative structure and iterative generation of text, images, audio, and video. We report quantitative outcomes on automatic story outline generation and qualitative observations of editing workflows. Finally, we discuss current limitations such as scalability to longer narratives and consistency across multiple nodes, and outline future work toward human-in-the-loop and user-centered creative AI tools.

Richa Gupta, Alexander Htet Kyaw

Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post-processing module identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO2e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs. Our qualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority of participants reported incorporating sustainability principles into their workflows in the third test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data-driven solutions in AI-assisted architectural design.

Alexander Htet Kyaw, Haotian Ma, Sasa Zivkovic et al.

Recent advances in augmented reality (AR) have enabled interactive systems that assist users in physical assembly tasks. In this paper, we present an AR-assisted assembly workflow that leverages object recognition and hand tracking to (1) identify custom components, (2) display step-by-step instructions, (3) detect assembly deviations, and (4) dynamically update the instructions based on users' hands-on interactions with physical parts. Using object recognition, the system detects and localizes components in real time to create a digital twin of the workspace. For each assembly step, it overlays bounding boxes in AR to indicate both the current position and the target placement of relevant components, while hand-tracking data verifies whether the user interacts with the correct part. Rather than enforcing a fixed sequence, the system highlights potential assembly errors and interprets user deviations as opportunities for iteration and creative exploration. A case study with LEGO blocks and custom 3D-printed components demonstrates how the system links digital instructions to physical assembly, eliminating the need for manual searching, sorting, or labeling of parts.

Alexander Htet Kyaw, Arvin Xu, Sasa Zivkovic et al.

Recent advancements in Augmented Reality (AR) have demonstrated applications in architecture, design, and fabrication. Compared to conventional 2D construction drawings, AR can be used to superimpose contextual instructions, display 3D spatial information and enable on-site engagement. Despite the potential of AR, the widespread adoption of the technology in the industry is limited by its precision. Precision is important for projects requiring strict construction tolerances, design fidelity, and fabrication feedback. For example, the manufacturing of glulam beams requires tolerances of less than 2mm. The goal of this project is to explore the industrial application of using multiple fiducial markers for high-precision AR fabrication. While the method has been validated in lab settings with a precision of 0.97, this paper focuses on fabricating glulam beams in a factory setting with an industry manufacturer, Unalam Factory.