The complex application scenarios have raised critical requirements for precise and generalizable gaze estimation methods. Recently, the pre-trained CLIP has achieved remarkable performance on various vision tasks, but its potentials have not been fully exploited in gaze estimation. In this paper, we propose a novel Differential Contrastive Training strategy, which boosts gaze estimation performance with the help of the CLIP. Accordingly, a Differential Contrastive Gaze Estimation network (DCGaze) composed of a Visual Appearance-aware branch and a Semantic Differential-aware branch is introduced. The Visual Appearance-aware branch is essentially a primary gaze estimation network and it incorporates an Adaptive Feature-refinement Unit (AFU) and a Double-head Gaze Regressor (DGR), which both help the primary network to extract informative and gaze-related appearance features. Moreover, the Semantic Difference-aware branch is designed on the basis of the CLIP's text encoder to reveal the semantic difference of gazes. This branch could further empower the Visual Appearance-aware branch with the capability of characterizing the gaze-related semantic information. Extensive experimental results on four challenging datasets over within and cross-domain tasks demonstrate the effectiveness of our DCGaze. The code is available at https://github.com/LinZhang-bjtu/DCGaze.

Gaze estimation task aims to predict a 3D gaze direction or a 2D gaze point given a face or eye image. To improve generalization of gaze estimation models to unseen new users, existing methods either disentangle personalized information of all subjects from their gaze features, or integrate unrefined personalized information into blended embeddings. Their methodologies are not rigorous whose performance is still unsatisfactory. In this paper, we put forward a comprehensive perspective named 'Disengage AND Integrate' to deal with personalized information, which elaborates that for specified users, their irrelevant personalized information should be discarded while relevant one should be considered. Accordingly, a novel Personalized Causal Network (PCNet) for generalizable gaze estimation has been proposed. The PCNet adopts a two-branch framework, which consists of a subject-deconfounded appearance sub-network (SdeANet) and a prototypical personalization sub-network (ProPNet). The SdeANet aims to explore causalities among facial images, gazes, and personalized information and extract a subject-invariant appearance-aware feature of each image by means of causal intervention. The ProPNet aims to characterize customized personalization-aware features of arbitrary users with the help of a prototype-based subject identification task. Furthermore, our whole PCNet is optimized in a hybrid episodic training paradigm, which further improve its adaptability to new users. Experiments on three challenging datasets over within-domain and cross-domain gaze estimation tasks demonstrate the effectiveness of our method.