  A reliable and comprehensive evaluation metric that aligns with manual
preference assessments is crucial for conversational head video synthesis
methods 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 demonstrate 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. Project Page: https://https://github.com/dc3ea9f/PreferenceScore.
