Face Consistency Benchmark for GenAI Video

2025-05-16

AI Summary

Poor cross-frame facial consistency of characters remains a critical bottleneck in AI-generated videos. This paper introduces the Facial Consistency Benchmark (FCB), the first standardized evaluation benchmark specifically designed for generative video. FCB formally defines and quantifies temporal consistency across four key facial attributes—identity, pose, expression, and illumination—and establishes a unified assessment framework integrating perceptual similarity with geometric stability. We conduct large-scale evaluations across state-of-the-art text- and image-to-video models, systematically revealing their degradation in long-term temporal consistency. FCB provides a reproducible, comparable, and diagnostic platform for model analysis and algorithmic improvement, thereby addressing a fundamental gap in the evaluation of facial consistency in generative video.

Technology Category

Computer Vision: Video Understanding & Activity AnalysisNatural Language Processing: GenerationHumans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsSocial Networks and Social Media: Generative AI / large language models and their impact on social systemsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Video generation driven by artificial intelligence has advanced significantly, enabling the creation of dynamic and realistic content. However, maintaining character consistency across video sequences remains a major challenge, with current models struggling to ensure coherence in appearance and attributes. This paper introduces the Face Consistency Benchmark (FCB), a framework for evaluating and comparing the consistency of characters in AI-generated videos. By providing standardized metrics, the benchmark highlights gaps in existing solutions and promotes the development of more reliable approaches. This work represents a crucial step toward improving character consistency in AI video generation technologies.

Problem

• Evaluating character consistency in AI-generated videos
• Standardizing metrics for face coherence assessment
• Addressing appearance gaps in current video generation models

Innovation

• Introduces Face Consistency Benchmark (FCB)
• Standardized metrics for character consistency
• Promotes reliable AI video generation approaches