The article introduces a novel method called Decaf, which captures face and hand interactions and facial deformations using monocular RGB videos. It addresses challenges such as depth ambiguity and lack of training datasets for non-rigid deformations. The method combines multiview capture and a position-based dynamics simulator to reconstruct the surface geometry. Neural networks are trained to extract 3D surface deformations, contact regions, and an interaction depth prior. The results show more plausible hand-face interactions compared to existing approaches.
Introducing Decaf: AI Framework for Face and Hand Interactions
Decaf is a cutting-edge artificial intelligence framework that captures face and hand interactions, including facial deformations, from monocular RGB videos. This innovative method solves the challenges of modeling dense, non-rigid object deformations and enhances applications like AR/VR, 3D virtual avatar communication, and character animations.
Key Features:
- Tracking human hands interacting with human faces in 3D from single monocular RGB videos
- Models hands as articulated objects that induce non-rigid facial deformations during interactions
- Utilizes a newly created dataset capturing hand-face motion and interaction, including realistic face deformations
- Relies on a variational auto-encoder and modules to guide the 3D tracking process
- Produces realistic 3D reconstructions of hands and faces compared to baseline methods
The reconstruction of both hands and the face simultaneously, considering the surface deformations resulting from their interactions, is crucial for enhancing realism in applications like avatar communication, virtual/augmented reality, and character animation. It also has implications for sign language transcription and driver drowsiness monitoring.
Challenges and Solutions:
Challenges in capturing face and hand interactions with non-rigid deformations from monocular RGB videos include the absence of a markerless RGB capture dataset and the inherent depth ambiguity of single-view RGB setups. Decaf addresses these challenges with a multiview capture setup, a position-based dynamics simulator, and a method called “skull-skin distance” to determine non-uniform stiffness values for the head mesh.
Practical Applications:
Decaf offers practical solutions for companies looking to evolve with AI and stay competitive. It can redefine your way of work by automating customer interactions, optimizing sales processes, and enhancing customer engagement. Some practical applications include:
- Automation Opportunities: Identify key customer interaction points that can benefit from AI
- KPI Definition: Ensure AI endeavors have measurable impacts on business outcomes
- AI Solution Selection: Choose tools that align with your needs and provide customization
- Gradual Implementation: Start with a pilot, gather data, and expand AI usage judiciously
If you want to learn more about Decaf and how AI can redefine your company’s processes, connect with us at hello@itinai.com. Stay updated on the latest AI research news and projects by joining our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter. You can also explore our AI Sales Bot, designed to automate customer engagement and manage interactions across all customer journey stages.
