OccFace: 基于点可见性的统一遮挡感知面部 landmark 检测
OccFace: Unified Occlusion-Aware Facial Landmark Detection with Per-Point Visibility
AI总结：
OccFace 提出了一种统一的遮挡感知面部 landmark 检测框架，通过联合预测 landmark 坐标和每个点的可见性，提升在遮挡和旋转条件下的鲁棒性。
摘要：
在遮挡条件下准确检测面部 landmark 仍具有挑战性，尤其是对于具有大外观变化和由旋转驱动的自遮挡的人类面孔。现有检测器通常在处理遮挡时隐式地定位 landmark，而没有预测每个点的可见性，这下游应用无法受益。我们提出了OccFace，一个针对通用人类面孔的遮挡感知框架，包括人类、风格化角色和其他非人类设计。OccFace采用统一的密集100点布局和基于热图的主干网络，并添加了一个遮挡模块，通过结合局部证据与跨landmark上下文，联合预测landmark坐标和每个点的可见性。可见性监督混合手动标签与landmark感知掩码，从掩码-热图重叠中推导出伪可见性。我们还创建了一个遮挡感知评估套件，报告可见 vs. 遮挡 landmark 的NME，并通过Occ AP、F1@0.5 和 ROC-AUC 进行可见性基准测试，同时包含标注有100点landmark 和每个点可见性的数据集。实验显示，在外部遮挡和大头旋转下具有改进的鲁棒性，尤其是在遮挡区域，同时保持可见landmark的准确性。
英文摘要：
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.
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