首个针对非参数化虚拟人动画的质量评估方法
Quality assessment of 3D human animation: Subjective and objective evaluation
- 构建用户主观评价数据集，驱动数据驱动评估框架
- 线性回归模型达到90%相关性，优于现有深度学习基线
- 适合虚拟现实、动画生成领域研究者参考
虚拟人动画在虚拟与增强现实中有广泛应用。尽管自动生成方法已发展成熟，其质量评估仍具挑战。现有任务导向评估指标依赖神经网络训练，但针对非参数化身体模型生成的虚拟人动画，尚无有效评估方法。本文提出首个此类质量评估方案：首先通过用户研究构建包含虚拟人动画及其主观真实感评分的数据集；其次利用该数据集训练感知评分预测模型。结果表明，在该数据集上训练的线性回归模型相关性达90%，显著优于当前最优的深度学习基线。
原文摘要 · Abstract (English)
Virtual human animations have a wide range of applications in virtual and augmented reality. While automatic generation methods of animated virtual humans have been developed, assessing their quality remains challenging. Recently, approaches introducing task-oriented evaluation metrics have been proposed, leveraging neural network training. However, quality assessment measures for animated virtual humans that are not generated with parametric body models have yet to be developed. In this context, we introduce a first such quality assessment measure leveraging a novel data-driven framework. First, we generate a dataset of virtual human animations together with their corresponding subjective realism evaluation scores collected with a user study. Second, we use the resulting dataset to learn predicting perceptual evaluation scores. Results indicate that training a linear regressor on our dataset results in a correlation of 90%, which outperforms a state of the art deep learning baseline.
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