Bliss Case Study
One Goal.
Bliss recognized that providing clients with a preview of their post-operative results had major impact on their experience satisfaction. However, no reliable solution existed to generate realistic, personalized post-surgery images, let alone an AI-driven one. The technology simply wasn’t there, leaving patients uncertain about their outcomes and impacting their overall experience.
Improve price sensitivity to maximize in-game store monetization.
To meet their need, we created an advanced AI imaging technology that allows patients to visualize their potential outcomes with high visual accuracy, offering a more informed and confident decision-making process before undergoing cosmetic surgery.
Our process began with in-depth research into stable diffusion and its applications, where we assessed existing solutions and identified limitations. We then evaluated multiple strategies based on feasibility, performance, and cost-effectiveness to ensure the final solution was both efficient and commercially viable. A working prototype was quickly built to validate core functionalities, identify roadblocks, and refine the model through rapid iteration.
Patients upload a high-quality photo, which is processed using state-of-the-art segmentation models to detect and isolate key features like skin and body structures. Our specialized AI model, fine-tuned with advanced techniques, then generates a predicted post-surgery outcome. Additional AI refinements ensure natural transitions, and the final result is a visually accurate post-surgery simulation, helping patients visualize their potential results.
The MVP underwent rigorous testing, focusing on metrics like image quality, generation time, and realism. We optimized performance through hyperparameter tuning, LoRA integration, and additional conditioning inputs to achieve more precise outputs. The final model was integrated into an easy-to-use system, with continuous monitoring and improvement based on real-world use.
