Abstract
Accurate facial landmark detection under occlusion remains challenging, especially for human-like faces with large appearance variation and rotation-driven self-occlusion. Existing detectors typically localize landmarks while handling occlusion implicitly, without predicting per-point visibility that downstream applications can benefits. We present OccFace, an occlusion-aware framework for universal human-like faces, including humans, stylized characters, and other non-human designs. OccFace adopts a unified dense 100-point layout and a heatmap-based backbone, and adds an occlusion module that jointly predicts landmark coordinates and per-point visibility by combining local evidence with cross-landmark context. Visibility supervision mixes manual labels with landmark-aware masking that derives pseudo visibility from mask-heatmap overlap. We also create an occlusion-aware evaluation suite reporting NME on visible vs. occluded landmarks and benchmarking visibility with Occ AP, F1@0.5, and ROC-AUC, together with a dataset annotated with 100-point landmarks and per-point visibility. Experiments show improved robustness under external occlusion and large head rotations, especially on occluded regions, while preserving accuracy on visible landmarks.
Abstract (translated)
在遮挡情况下进行精确的面部特征点检测仍然具有挑战性，尤其是在外观变化大且因旋转导致自我遮挡的人脸（包括真实人类、拟人化角色及其他非人类设计）上。现有的检测器通常是在处理遮挡时隐式地定位特征点，而不预测每个特征点的可见性，后者对下游应用是有益的信息。我们提出了OccFace，这是一个针对通用人脸的遮挡感知框架，涵盖了人类、拟人化人物以及其他非人类的设计。 OccFace采用统一的密集100个特征点布局和基于热图的骨干网络，并添加了一个遮挡模块，该模块通过结合局部证据与跨特征点上下文来共同预测特征点坐标和每个特征点的可见性。可见性监督结合了手动标签以及从掩码-热图重叠中推导出伪可见性的特征点感知掩码。我们还创建了一套包含100个特征点标注及每个特征点可见性的遮挡感知评估工具，该工具报告可视与不可视特征点的NME，并使用Occ AP、F1@0.5和ROC-AUC来评估可见性。 实验表明，在外部遮挡和头部大旋转的情况下具有更好的鲁棒性，尤其是在被遮挡区域中表现更为突出，同时保持了在可视特征点上的准确性。
URL
https://arxiv.org/abs/2602.10728
