构建高分辨率人脸光流数据集与模型，提升微表情识别效果
SRFlow: A Dataset and Regularization Model for High-Resolution Facial Optical Flow via Splatting Rasterization
- 用高斯点阵渲染生成高分辨率人脸光流数据
- 新模型在微表情数据集上误差降低48%
- 适合做面部动作分析和微表情识别的研究者
人脸光流支持多种面部运动分析任务，但缺乏高分辨率数据集限制了该领域进展。本文提出基于点阵渲染的高分辨率人脸光流数据集SRFlow，以及针对该数据集设计的光流模型SRFlowNet。通过差分或Sobel算子计算掩码与梯度，引入定制化正则化损失，有效抑制无纹理或重复图案区域的高频噪声与大尺度误差。实验表明，使用SRFlow训练可使各类光流模型端点误差（EPE）降低42%（从0.5081降至0.2953）。结合该数据集时，SRFlowNet在三个微表情数据集的组合测试中F1分数提升48%（从0.4733增至0.6947），显著提升微表情识别性能。
原文摘要 · Abstract (English)
Facial optical flow supports a wide range of tasks in facial motion analysis. However, the lack of high-resolution facial optical flow datasets has hindered progress in this area. In this paper, we introduce Splatting Rasterization Flow (SRFlow), a high-resolution facial optical flow dataset, and Splatting Rasterization Guided FlowNet (SRFlowNet), a facial optical flow model with tailored regularization losses. These losses constrain flow predictions using masks and gradients computed via difference or Sobel operator. This effectively suppresses high-frequency noise and large-scale errors in texture-less or repetitive-pattern regions, enabling SRFlowNet to be the first model explicitly capable of capturing high-resolution skin motion guided by Gaussian splatting rasterization. Experiments show that training with the SRFlow dataset improves facial optical flow estimation across various optical flow models, reducing end-point error (EPE) by up to 42% (from 0.5081 to 0.2953). Furthermore, when coupled with the SRFlow dataset, SRFlowNet achieves up to a 48% improvement in F1-score (from 0.4733 to 0.6947) on a composite of three micro-expression datasets. These results demonstrate the value of advancing both facial optical flow estimation and micro-expression recognition.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。