Landmark-Guided Gaze Estimation via Conditional Keypoint Generation and Cross-Attention Fusion

In gaze estimation, existing mainstream methods face significant challenges in capturing the fine-grained structures of eye regions, particularly in the absence of explicit geometric prior information, which hampers gaze prediction accuracy. To address this limitation, we propose the landmark-guided gaze estimation network (LGNet), a gaze estimation method guided by keypoints, which effectively incorporates geometric prior information to enhance estimation performance. The proposed method begins by training an eye-keypoint generator on the synthetic UnityEyes dataset using a Conditional Variational Autoencoder (CVAE). Next, we introduce a Symmetric Spatial Feature Fusion module (SSFF), combined with a dual-stream cross-attention mechanism, to achieve semantic alignment between the keypoint features and the facial image features extracted using ResNet50. Furthermore, we propose a Gated Channel Reweighting module (GCR) to suppress redundant information and amplify the critical features, thereby enhancing the model’s overall response. Experimental results demonstrate that LGNet outperforms existing methods on three benchmark datasets. The code for this research has been made publicly available.