Proposed: Lip Sync to Facial Action Coding System

Context

This proposal involves the development of a speech-to-FACS (Facial Action Coding System) mapping pipeline to capture real-world 3D facial movements synchronized with speech. Metrics include the accuracy of lip-sync, the diversity of speakers and phrases, and the scalability of the system. A robust speech-to-FACS mapping system can enhance virtual avatars, animation, and human-computer interaction. This proposal addresses the growing need for realistic virtual characters in industries like entertainment, education, and healthcare, making it timely and strategically relevant.

Problem Statement

The primary issues causing operational challenges are:

Lack of a robust speech-to-FACS system for capturing 3D facial movements synchronized with speech.
Difficulty in collecting diverse, high-quality training/validation data.
Risk of overfitting to lab data and insufficient real-world noise augmentation.

Proposed Solution

A speech-to-FACS pipeline that maps speech to 3D facial movements using multimodal alignment and reinforcement learning. Key technical steps include:

Converting FaceMotion3D blend shapes renderings with markers at vertex positions to Keypoint COCO JSON format.
Leveraging “Accent Tag” phonetic-rich sentences for training.
Aligning audio to FACS keypoints via a multimodal large language model.
Applying reinforcement learning to generate speech matching COCO Full Body Face data from masked audio.

Implementation Steps:

Collect and preprocess data (blend shape conversion, speaker recordings).
Train a multimodal model for audio-FACS alignment.
Implement reinforcement learning for speech generation.

Implementation Plan

Phase 1: Data Collection & Preprocessing
- Convert FaceMotion3D blend shapes to COCO JSON.
- Record diverse speakers performing phonetic-rich phrases.
Phase 2: Model Development & Training
- Train multimodal model for audio-to-FACS alignment.
- Develop reinforcement learning pipeline for masked audio matching.
Validation:
- Success Criteria: High lip-sync accuracy (>90%) and positive user study feedback.
- Failure Threshold: <10% improvement in accuracy or negative user assessments.

Benefits

Improved lip-sync accuracy for virtual avatars and animations.
Enhanced user engagement in entertainment, education, and healthcare.
Scalable data collection and model development processes.

Risks and Limitations

Overfitting to lab data without real-world noise augmentation.
Challenges in acquiring diverse, high-quality training data.
Potential inaccuracies in FaceMotion3D animations.

Alternatives Considered

Option	Pros	Cons
Existing speech-to-text systems	Faster development	Lower accuracy and flexibility
Custom speech-to-FACS system	Higher accuracy	Higher cost and longer timeline

When to Avoid This Solution

Avoid if high realism is unnecessary or development costs/time are prohibitive.

Organizational Alignment

Supports organizational goals in virtual character innovation and aligns with priorities for experience enhancement.