IP-FaceDiff: Identity-Preserving Facial Video Editing with Diffusion

2025-01-13

AI Summary

Existing facial video editing methods suffer from identity distortion, low editing fidelity, temporal incoherence, high computational cost, and poor generalization to diverse text prompts—particularly under complex expressions and multi-pose dynamic sequences. To address these challenges, we propose the first text-driven facial video editing framework built upon a pre-trained text-to-image (T2I) diffusion model. Our approach innovatively integrates identity-aware fine-tuning, latent-space local editing, and explicit temporal consistency constraints, enabling fine-grained local manipulation while preserving cross-frame identity stability. Crucially, it supports zero-shot text prompt generalization—requiring no additional training to interpret diverse semantic instructions. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches across identity fidelity, visual quality, and temporal coherence, achieves an 80% speedup in editing latency, and exhibits significantly enhanced robustness to multi-pose configurations, intricate motions, and rich facial expressions.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Facial video editing has become increasingly important for content creators, enabling the manipulation of facial expressions and attributes. However, existing models encounter challenges such as poor editing quality, high computational costs and difficulties in preserving facial identity across diverse edits. Additionally, these models are often constrained to editing predefined facial attributes, limiting their flexibility to diverse editing prompts. To address these challenges, we propose a novel facial video editing framework that leverages the rich latent space of pre-trained text-to-image (T2I) diffusion models and fine-tune them specifically for facial video editing tasks. Our approach introduces a targeted fine-tuning scheme that enables high quality, localized, text-driven edits while ensuring identity preservation across video frames. Additionally, by using pre-trained T2I models during inference, our approach significantly reduces editing time by 80%, while maintaining temporal consistency throughout the video sequence. We evaluate the effectiveness of our approach through extensive testing across a wide range of challenging scenarios, including varying head poses, complex action sequences, and diverse facial expressions. Our method consistently outperforms existing techniques, demonstrating superior performance across a broad set of metrics and benchmarks.

Problem

• Facial Video Editing
• Performance Improvement
• Complex Facial Expressions

Innovation

• IP-FaceDiff
• Diffusion Technique
• Customizable Facial Editing