THQA: A Perceptual Quality Assessment Database for Talking Heads
Yingjie Zhou Zicheng Zhang Wei Sun Xiaohong Liu Xiongkuo Min Zhihua Wang Xiao-Ping Zhang Guangtao Zhai
In the realm of media technology, digital humans have gained prominence due
to rapid advancements in computer technology. However, the manual modeling and
control required for the majority of digital humans pose significant obstacles
to efficient development. The speech-driven methods offer a novel avenue for
manipulating the mouth shape and expressions of digital humans. Despite the
proliferation of driving methods, the quality of many generated talking head
(TH) videos remains a concern, impacting user visual experiences. To tackle
this issue, this paper introduces the Talking Head Quality Assessment (THQA)
database, featuring 800 TH videos generated through 8 diverse speech-driven
methods. Extensive experiments affirm the THQA database's richness in character
and speech features. Subsequent subjective quality assessment experiments
analyze correlations between scoring results and speech-driven methods, ages,
and genders. In addition, experimental results show that mainstream image and
video quality assessment methods have limitations for the THQA database,
underscoring the imperative for further research to enhance TH video quality
assessment. The THQA database is publicly accessible at
https://github.com/zyj-2000/THQA.