SBIR Phase I: End-to-End Platform to Generate Custom-Fitted Garments Via Body Scanning, Shape Analysis and Fit Algorithms, and On-Demand 3D Knitting

Award ID: 2451586

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate a novel AI-driven mass-customization technology for producing custom-fit garments. This project utilizes a proprietary AI-driven technology to translate phone-based body scans into 3D knitting instructions that can be individually produced. This technology is scalable and can be integrated into standard 3D knitting workflows, make use of knitting as a low-waste, additive manufacturing technique. Many markets and garments require customization including prosthetic interface garments, undergarments for breast cancer survivors or mastectomy patients, and post-surgical or custom compression-fitted pieces that form to the unique contours of a patients body. By offering an individualized custom-fit with full workflow from body-scan to 3D knit, this technology is well-positioned to bring manufacturing local and reduce waste and high rate of returns common in todays garment and fashion e-commerce industries. This Small Business Innovation Research (SBIR) Phase I project will develop a novel AI-driven technology to automate the generation of custom-fitted garments for superior fit. Phase I will streamline the translation of body scan data into 3D knitting instructions for an optimal fit while ensuring compatibility with industry-standard knitting machines for rapid prototype and acceleration to market. Objective 1 will focus on automating the process of translating body scans into ready-to-print instructions for the knitting machine. Objective 2 focuses on developing a user-friendly interface for seamless body scanning, including optimizing the capture and processing capabilities of the platform, precision tracking of 3D reconstruction, and improving instructions for the customer. The platforms usability will be assessed through testing with a broad sample population, including those with garment fit challenges. In Objective 3, validation of the superior fit of custom-made garments will be assessed. Users will provide feedback on the scanning process and the fit of their custom-fabricated garments versus an off-the-shelf brand, and iterative refinements to the fit algorithm will be made based on both their feedback and insights from an expert fit consultant. Together, this work will de-risk the technological aspects critical to mass customization and the user experience priming the company for commercialization at scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Vincent Lee