LATENT SPACE EDITING AND NEURAL ANIMATION TO GENERATE HYPERREAL SYNTHETIC FACES

Smart overview of the Invention

Latent space manipulation and neural animation are utilized to create hyperreal synthetic faces from unaltered video content. By training machine learning models on video data of subjects making mouth-generated sounds, these technologies can generate synthetic faces that convincingly mimic real expressions and movements. The resulting synthetic faces can then be integrated into altered video content, allowing for realistic lip-syncing and language translation.

Challenges with Existing Technologies

Current methods for generating synthetic faces often lead to unnatural facial expressions, failing to meet the hyperrealism standard. Although some hyperreal content can be produced, it is not consistently reproducible at scale. The techniques described aim to overcome these limitations by employing latent space manipulation and neural animation to enhance the realism of synthetic faces.

Process of Generating Synthetic Faces

The generation process begins with input audio data that corresponds to mouth-generated sounds. This audio is used to animate a 3D model of the subject's face, aligning it with the 2D representations in the original video frames. A trained machine learning model then generates instances of the synthetic face based on these aligned models, ensuring that the facial expressions appear natural and realistic.

Applications of the Technology

The described techniques have various applications, including lip-syncing and language translation. For instance, they can make it appear that a subject is speaking in a different language by matching synthetic mouth movements to translated audio. This capability enhances the viewing experience in contexts like the metaverse, where realistic interactions are essential.

Improving Synthetic Content Generation

The proposed methods enhance the quality of synthetic faces by making their expressions more natural through advanced manipulation techniques. Unlike existing approaches that rely on retraining models or requiring exaggerated expressions during recording, this technology facilitates scalable and repeatable production of hyperreal content without compromising quality or realism.