ClipFaceFusion multi modal diffusion for high fidelity facial generation and modification.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Existing face generation methods struggle with multi-modal inputs and precise attribute control.
• DiffusionCLIP and StyleCLIP have limitations in cross-modal consistency and semantic regulation.

Purpose of the Study:

• To introduce ClipFaceFusion, a novel diffusion-based framework for photorealistic face generation and manipulation using multi-modal inputs.
• To enable precise control over facial attributes like age and emotion through explicit semantic signals.

Main Methods:

• Developed a trainable multi-signal fusion module integrating text, audio, and reference images.
• Implemented novel consistency loss functions for audio-visual alignment and age/emotion regulation.
• Utilized a Denoising Diffusion Implicit Models (DDIM) framework with a multi-tiered identity preservation system (ArcFace, perceptual loss).

Main Results:

• ClipFaceFusion outperforms DiffusionCLIP and StyleCLIP in generating realistic faces with accurate age and emotional expressions.
• Demonstrated superior Cross-Modal Coherence (CMC) and reduced visual artifacts compared to existing techniques.
• Achieved precise attribute regulation and identity conservation during image modification.

Conclusions:

• ClipFaceFusion establishes a new benchmark for personalized face synthesis and manipulation by seamlessly integrating multi-modal inputs.
• The framework offers significant potential for applications in media creation, psychological simulations, and historical facial reconstruction.
• Achieved superior control and realism in face generation, addressing limitations of prior methodologies.