捕捉动态：4D面部表情强度数据集
Capturing Dynamics: The 4D Facial Expression Intensity Dataset
- École Centrale Nantes（南特中央理工学院）
- CNRS（法国国家科学研究中心）
- LS2N（LS2N实验室）
- Nantes Université（南特大学）
- CAPACITÉS SAS（CAPACITÉS公司）
- Institut universitaire de France (IUF)（法国大学研究院）
- University of Oulu（奥卢大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出4D面部表情强度数据集（4DFEID），包含2,869个3D网格序列和90,000多个主观评分，并验证时空图模型优于传统方法，为动态3D表情强度感知研究提供新资源。
AI中文摘要：
面部表情强度的估计与分析在情感交流和人机交互中起着至关重要的作用。以往的研究主要集中于从帧级2D表示中检测和估计面部表情强度。然而，这种局限性限制了对真实世界面部表情的全面理解，因为面部表情本质上是3D的且在时间上是连续的。本文通过引入4D面部表情强度数据集（4DFEID）来研究面部表情强度的感知。我们采用参数化人脸模型，并编译了总共2,869个具有受控几何变化的面部网格序列，生成了具有不同峰值强度和身份属性的4D数据实例。使用李克特量表，我们通过众包平台收集了超过90,000个主观强度感知评分。我们探索了多种架构和聚合方法，以建立新数据集上情节强度估计的基线，结果表明时空图模型始终优于传统的帧聚合方法。与依赖2D静态图像的现有数据集相比，所提出的4D-FEID数据集为社区提供了一个独特且至关重要的资源，用于通过动态3D刺激研究面部表情强度的感知。通过提供高保真、时空连贯的面部数据，4D-FEID为更细致和自然的表情分析研究建立了新的基础，从而填补了当前情感计算和人机交互研究领域中的空白。该数据集可在链接处获取。
英文摘要：
The estimation and analysis of facial expression intensity play a crucial role in affective communication and human-computer interaction. Previous research has primarily focused on detecting and estimating facial expression intensity from frame-level 2D representations. However, this limitation restricts a comprehensive understanding of real-world facial expressions, as they are inherently 3D and temporally continuous. This paper investigates the perception of facial expression intensity by introducing the 4D Facial Expression Intensity Dataset (4DFEID). We employ a parametric face model and compile a total of 2,869 mesh sequences with controlled geometric variations, generating 4D data instances with diverse peak intensities and identity attributes. Using a Likert scale, we collect more than 90,000 subjective intensity perception ratings via a crowdsourcing platform. We explore various architectures and aggregation methods to establish baselines for episode intensity estimation on the new dataset, revealing that spatial-temporal graph models consistently outperform traditional frame-aggregation methods. In contrast to existing datasets that rely on 2D static imagery, the proposed 4D-FEID dataset provides the community with a unique and vital resource for investigating the perception of facial expression intensity through the use of dynamic 3D stimuli. By offering high-fidelity, spatio-temporally coherent facial data, 4D-FEID establishes a new foundation for research into more nuanced and naturalistic expression analysis, thereby addressing a gap in the current landscape of affective computing and human-computer interaction studies. The dataset is available at link.
