18734327. Photorealistic Talking Faces from Audio simplified abstract (Google LLC)
Contents
- 1 Photorealistic Talking Faces from Audio
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Photorealistic Talking Faces from Audio - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Questions about 3D Talking Face Generation Framework for Audio Input
- 1.10 Original Abstract Submitted
Photorealistic Talking Faces from Audio
Organization Name
Inventor(s)
Vivek Kwatra of Saratoga CA (US)
Christian Frueh of Mountain View CA (US)
Avisek Lahiri of West Bengal (IN)
John Lewis of Mountain View CA (US)
Photorealistic Talking Faces from Audio - A simplified explanation of the abstract
This abstract first appeared for US patent application 18734327 titled 'Photorealistic Talking Faces from Audio
Simplified Explanation
This patent application presents a framework for creating realistic 3D talking faces based solely on audio input. It also includes methods for integrating these generated faces into existing videos or virtual environments. The technology breaks down faces from videos into separate components like 3D geometry, head pose, and texture, making it easier to predict and generate lifelike faces.
- The framework generates photorealistic 3D talking faces using only audio input.
- It allows for the insertion of these generated faces into pre-existing videos or virtual environments.
- Faces from videos are decomposed into distinct elements like 3D face shape and 2D texture for accurate prediction.
- An auto-regressive approach is used to stabilize temporal dynamics by conditioning the model on its previous visual state.
- Face illumination is captured using audio-independent 3D texture normalization in the model.
Potential Applications
The technology can be used in various applications such as:
- Virtual reality and augmented reality experiences
- Video conferencing and telecommunication
- Gaming and animation industries
- Virtual assistants and chatbots
- Entertainment and content creation
Problems Solved
This technology addresses the following issues:
- Generating realistic 3D talking faces from audio input
- Integrating generated faces seamlessly into existing videos or virtual environments
- Predicting and stabilizing temporal dynamics for lifelike facial animations
- Capturing face illumination accurately using audio-independent 3D texture normalization
Benefits
The benefits of this technology include:
- Enhanced user experience in virtual environments and video communication
- Realistic and expressive 3D talking faces for entertainment and gaming
- Improved efficiency in content creation and animation
- Seamless integration of generated faces into various applications
- Accurate prediction and stabilization of facial animations
Commercial Applications
- "3D Talking Face Generation Framework for Audio Input" can revolutionize the virtual reality and gaming industries by providing realistic and interactive avatars.
- This technology can also be utilized in video conferencing platforms to enhance user engagement and communication.
- Content creators and animators can benefit from the efficiency and accuracy of generating lifelike 3D faces for their projects.
Questions about 3D Talking Face Generation Framework for Audio Input
What are the potential applications of this technology in the entertainment industry?
The technology can be used in various applications such as virtual reality experiences, gaming, animation, and content creation to enhance user engagement and create realistic characters.
How does the auto-regressive approach used in this framework help stabilize temporal dynamics?
The auto-regressive approach conditions the model on its previous visual state, allowing for smoother transitions and more natural facial animations over time.
Original Abstract Submitted
Provided is a framework for generating photorealistic 3D talking faces conditioned only on audio input. In addition, the present disclosure provides associated methods to insert generated faces into existing videos or virtual environments. We decompose faces from video into a normalized space that decouples 3D geometry, head pose, and texture. This allows separating the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. To stabilize temporal dynamics, we propose an auto-regressive approach that conditions the model on its previous visual state. We also capture face illumination in our model using audio-independent 3D texture normalization.
