论文标题
Flnerf：神经辐射场中的3D面部标志性估计
FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields
论文作者
论文摘要
本文介绍了直接预测神经辐射场（NERFS）上的3D面部标志的第一批重要工作。我们的3D粗到1个面部标记NERF（FLNERF）模型有效地从给定的面部nerf样品中采样，具有单个面部特征，以进行精确的地标检测。表达增强量适用于面部特征，以模拟大型情绪范围，包括夸张的面部表情（例如，脸颊吹，张开的嘴巴，眨眼，眨眼）进行训练。定性和定量比较与相关的最先进的面部标志性估计方法证明了FLNERF的功效，这有助于下游任务，例如使用我们的NERF地标进行高质量的面部编辑和与直接控制的交换。代码和数据将可用。 github链接：https：//github.com/zhang1023/flnerf。
This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs). Our 3D coarse-to-fine Face Landmarks NeRF (FLNeRF) model efficiently samples from a given face NeRF with individual facial features for accurate landmarks detection. Expression augmentation is applied to facial features in a fine scale to simulate large emotions range including exaggerated facial expressions (e.g., cheek blowing, wide opening mouth, eye blinking) for training FLNeRF. Qualitative and quantitative comparison with related state-of-the-art 3D facial landmark estimation methods demonstrate the efficacy of FLNeRF, which contributes to downstream tasks such as high-quality face editing and swapping with direct control using our NeRF landmarks. Code and data will be available. Github link: https://github.com/ZHANG1023/FLNeRF.
