TempoSyncDiff: Distilled Temporally-Consistent Diffusion for Low-Latency Audio-Driven Talking Head Generation

Researchers introduce TempoSyncDiff, a new AI framework that uses distilled diffusion models to generate realistic talking head videos from audio with significantly reduced computational latency. The system addresses key challenges in AI-driven video synthesis including temporal instability, identity drift, and audio-visual alignment while enabling deployment on edge computing devices.

• →TempoSyncDiff uses teacher-student distillation to reduce inference steps while maintaining quality in talking head video generation.
• →The framework incorporates identity anchoring and temporal regularization to prevent identity drift and frame flickering.
• →The system demonstrates feasibility for edge computing deployment with substantially lower latency than traditional diffusion models.
• →Viseme-based audio conditioning provides improved lip synchronization control for realistic speech synthesis.
• →Testing on LRS3 dataset shows the distilled model retains much of the teacher model's reconstruction quality.