FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields

• URL: http://arxiv.org/abs/2211.11202v3
• Date: Fri, 16 Jun 2023 10:52:13 GMT
• Title: FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields
• Authors: Hao Zhang, Tianyuan Dai, Yu-Wing Tai, Chi-Keung Tang
• Abstract summary: 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.
• Score: 64.17946473855382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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