This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture.

This study systematizes the form generation process using machine learning-driven motion-tracking data and investigates the interrelationships between the characteristics of generated data and forms generated according to data-processing methods. Through the vision-based machine learning motion estimation (VideoPose3D) algorithm, 3D motion data are extracted from 2D video and categorized into point (joint), curve (bone), and boundary (range of motion) types. Furthermore, this study analyzes the form generation characteristics and limitations associated with each type of motion-tracking data derived from dynamic-to-dynamic physical activities with postural transitions. A data-processing methodology based on artistic practice from prior research is applied. The characteristics of generated data and the morphological characteristics of generated forms are then analyzed according to non-processed and processed methods. Results suggest a potential correlative tendency between the characteristics and generated forms of each type of motion data value information. A bidirectional complementary relationship exists between non-processed and processed motion-tracking data. The data-based form generation methodology demonstrates potential applicability in architectural design. This study expands design possibilities by supporting decisions early in the architectural design process and immediately generating diverse alternatives; it also proposes a standardized framework for a universal data-centric design process applicable to diverse data types, including motion data.