A reliable and comprehensive evaluation metric that aligns with manual
preference assessments is crucial for conversational head video synthesis
method development. Existing quantitative evaluations often fail to capture the
full complexity of human preference, as they only consider limited evaluation
dimensions. Qualitative evaluations and user studies offer a solution but are
time-consuming and labor-intensive. This limitation hinders the advancement of
conversational head generation algorithms and systems. In this paper, we
propose a novel learning-based evaluation metric named Preference Score (PS)
for fitting human preference according to the quantitative evaluations across
different dimensions. PS can serve as a quantitative evaluation without the
need for human annotation. Experimental results validate the superiority of
Preference Score in aligning with human perception, and also demonstrates
robustness and generalizability to unseen data, making it a valuable tool for
advancing conversation head generation. We expect this metric could facilitate
new advances in conversational head generation.

我们提出了一种名为 Preference Score（PS）的新型基于学习的评估指标，用于对人类偏好进行定量评估，验证其在与人类感知的一致性、对未见数据的鲁棒性和泛化能力方面的优越性，对推进对话头生成具有重要价值。