The rapid advancement of Artificial Intelligence Generated Content (AIGC)
technology has propelled audio-driven talking head generation, gaining
considerable research attention for practical applications. However,
performance evaluation research lags behind the development of talking head
generation techniques. Existing literature relies on heuristic quantitative
metrics without human validation, hindering accurate progress assessment. To
address this gap, we collect talking head videos generated from four generative
methods and conduct controlled psychophysical experiments on visual quality,
lip-audio synchronization, and head movement naturalness. Our experiments
validate consistency between model predictions and human annotations,
identifying metrics that align better with human opinions than widely-used
measures. We believe our work will facilitate performance evaluation and model
development, providing insights into AIGC in a broader context. Code and data
will be made available at this https URL

通过进行控制性的心理物理实验，我们验证了模型预测与人工注释之间的一致性，鉴定出相对于广泛使用的度量方法更能与人类意见相符的评估指标，以填补评估研究在视觉质量、嘴唇音频同步和头部移动自然性方面的空白。我们相信我们的工作将促进性能评估和模型开发，为人工智能生成内容在更广泛的背景下提供洞察。