Pose and Expression Change
In a recent project, our team successfully implemented a groundbreaking solution that combines artificial intelligence and art to enhance user experiences on a custom chatbot platform. We developed a completely new AI system that introduces unique features, meeting the growing demand for personalization and creativity from users.
Multi Artstyle Generative
To realize the client's vision, we trained 30 specialized LoRAs (Low-Rank Adaptation) models, each representing a distinct artistic style. These models not only meet high aesthetic standards but are also optimized for seamless integration into the chatbot AI system.
The system is designed to process one input image (avatar) and customize it through three main features:
Background Customization: Users can choose from various backgrounds to create a context that matches the character's story or personality.
Expression & Pose Adjustment: The AI can modify facial expressions and generate poses that align with specific emotional states, making the character more dynamic.
Outfit Customization: The technology enables AI to change the character's outfit while maintaining the original artistic style, offering flexibility and diversity in personalization.
Built upon extensive research on human-chatbot interactions, our solution goes beyond image generation. It significantly enhances the communication experience between users and the AI system, delivering a truly transformative product.
During development, we encountered multiple challenges, ranging from optimizing LoRAs to ensure minimal resource consumption to maintaining artistic consistency that met client requirements. Among the most complex problems was character consistency, a notoriously difficult issue in Gen-AI image generation.
Maintaining character consistency is often a frustrating challenge in the field. While there are numerous repositories on GitHub addressing this problem, most fail to deliver reliable results beyond a handful of cases. Many solutions found online require extensive manual processing, making them impractical for automation. Transforming this into an automated workflow proved to be exceptionally difficult.
To overcome this, we developed a robust solution by combining multiple advanced techniques:
Faceswap: For maintaining facial fidelity and consistency across variations.
ControlNet and IPAdapter: Applied to handle specific elements within the image, ensuring coherence and customization across backgrounds, poses, and outfits.
Acceleration Technologies: Integrated to optimize resource utilization and reduce generation time.
