THQA: A Perceptual Quality Assessment Database for Talking Heads
This work addresses the need for better quality evaluation in digital human media, which impacts user visual experiences, though it is incremental as it focuses on dataset creation and benchmarking.
The authors tackled the problem of assessing the perceptual quality of talking head videos by introducing the THQA database, which contains 800 videos generated by 8 speech-driven methods, and found that existing quality assessment methods are inadequate for this task.
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.