work provides a solution that could already be adopted by industry leaders such as Meta, Snapchat, and other companies with large user bases. “If you're trying to store one million users’ models, that requires a huge storage cost,” Luchao points out. “It’s going to be data-hungry. The computational cost is massive. We’re trying to create these solutions that provide each person or company a usable way. That’s something to be proud of.” The work has already paved the way for one extension, which was also accepted at WACV this year, focusing on continual learning of personalized generative face models. This allows users to update their AI-generated faces as they take new photos without incurring high retraining costs. “We have a lot of follow-up work trying to explore how you make such solutions feasible and scalable and to work over time,” Luchao adds. “I really believe this direction that decentralizes and democratizes the AI solution for each user is pretty promising. Especially how users can interact with generative AI solutions. That's an interesting direction for future researchers and industrial applications.” To learn more about My3DGen, visit Oral Session 5.1: 3D Computer Vision V today (Sunday) from 14:00 to 15:00. 13 DAILY WACV Sunday My3DGen
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