2026-03-02 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research-news/202603/t20260303_1151362.shtml
- https://ieeexplore.ieee.org/document/11414185
CF-GAT: 高精度な非順序顔点群ランドマーク検出のための曲率融合グラフアテンションネットワーク CF-GAT: Curvature-fused Graph Attention Network for High-Precision Unordered Facial Point Cloud Landmark Detection
Juncheng Han; Yuping Ye; Xintong Yang; Juan Zhao; Zhan Song
IEEE Transactions on Circuits and Systems for Video Technology Published:26 February 2026
DOI:https://doi.org/10.1109/TCSVT.2026.3668485
Abstract
Due to the lack of large-scale, accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of texture mapping and the difference between digital faces and actual faces. To tackle these challenges, we built a large-scale, high-precision, and accurately annotated 3D facial database using a structured light system. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network to directly predict landmark coordinates from 3D point clouds. Firstly, we extracted a simplifying point set carrying curvature information from the original 3D facial point cloud via geometric point sampling. Then, we incorporated curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the network. We evaluated the performance of the method on BU-3DFE, FaceScape and our custom dataset. Compared to existing facial landmark detection algorithms, our model achieved higher accuracy. To facilitate future research on face related applications, we have made the database available on Github 1.
