A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos

• URL: http://arxiv.org/abs/2403.06421v1
• Date: Mon, 11 Mar 2024 04:13:38 GMT
• Title: A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos
• Authors: Weixia Zhang and Chengguang Zhu and Jingnan Gao and Yichao Yan and Guangtao Zhai and Xiaokang Yang
• Abstract summary: We collect talking head videos generated from four generative methods. We 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.
• Score: 81.54357891748087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 https://github.com/zwx8981/ADTH-QA.

Related papers

• SONAR: A Synthetic AI-Audio Detection Framework and Benchmark [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark. It aims to provide a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content. It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based deepfake detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction [0.0]
  This paper reports a novel method for human-AI interaction by developing a direct brain-AI interface. We discuss a novel AI model, called MindSpeech, which enables open-vocabulary, continuous decoding for imagined speech. We demonstrate significant improvements in key metrics, such as BLEU-1 and BERT P scores, for three out of four participants.
  arXiv  Detail & Related papers  (2024-07-25T16:39:21Z)
• THQA: A Perceptual Quality Assessment Database for Talking Heads [56.42738564463101]
  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. This paper introduces the Talking Head Quality Assessment (THQA) database, featuring 800 TH videos generated through 8 diverse speech-driven methods.
  arXiv  Detail & Related papers  (2024-04-13T13:08:57Z)
• AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension [95.8442896569132]
  We introduce AIR-Bench, the first benchmark to evaluate the ability of Large Audio-Language Models (LALMs) to understand various types of audio signals and interact with humans in the textual format. Results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation.
  arXiv  Detail & Related papers  (2024-02-12T15:41:22Z)
• Learning and Evaluating Human Preferences for Conversational Head Generation [101.89332968344102]
  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.
  arXiv  Detail & Related papers  (2023-07-20T07:04:16Z)
• Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
  Modelling of early language acquisition aims to understand how infants bootstrap their language skills. Recent developments have enabled the use of more naturalistic training data for computational models. It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
  arXiv  Detail & Related papers  (2023-05-03T08:25:37Z)
• What comprises a good talking-head video generation?: A Survey and Benchmark [40.26689818789428]
  We present a benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies. We propose new metrics or select the most appropriate ones to evaluate results in what we consider as desired properties for a good talking-head video.
  arXiv  Detail & Related papers  (2020-05-07T01:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.