眼动生物特征（EMB）是一种新兴的行为模态，适用于用户身份认证，尤其在虚拟现实（VR）与增强现实（AR）系统中，其注视动力学蕴含显著的被试特异性特征。然而，构建鲁棒的EMB系统需大量多样且高质量的注视记录，而此类数据采集成本高昂，且往往难以满足模型开发所需的规模。生成式模型可缓解数据稀缺问题，但现有方法或仅合成通用注视行为，或主要依据身份进行信号个性化，未能联合建模用户的任务与主观状态。因此，所生成信号虽在视觉上逼真，却可能无法保留生物特征识别所需的行为特性。为解决该局限，我们提出 EyeMakeYou——一种面向被试特异性的高频注视合成的多条件去噪扩散框架。EyeMakeYou 以去除身份信息的参考轨迹为输入，生成时长5秒、采样率1000 Hz的双变量注视速度序列，并在去噪过程中联合条件化于身份嵌入、任务嵌入，以及关于整体难度、精神疲劳度和眼部疲劳度的自评量表评分。其优化目标融合了扩散噪声预测与身份保持，并引入多分辨率谱损失、漂移一致性损失及事件加权的局部平滑性损失。在 GazeBase 数据集上的实验表明，EyeMakeYou 在中位空间精度及嵌入特征空间中真实–合成样本相似性方面均优于现有生成方法，同时保留了若干主观报告与眼动特征之间依赖于任务的关联性。上述结果支持条件扩散作为面向生物特征识别与交互应用的注视数据增强的一种实用方法。

Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, where gaze dynamics contain distinctive subject-specific features. However, robust EMB systems require diverse, high-quality gaze recordings that are expensive to collect and often unavailable at the scale needed for model development. Generative models can mitigate data scarcity, but existing methods either synthesize generic gaze behavior or personalize signals primarily by identity, without jointly representing the user's task and subjective state. Consequently, generated signals may appear visually realistic while failing to retain the behavioral properties required for biometric applications. To address this limitation, we propose EyeMakeYou, a multi-conditional denoising diffusion framework for subject-specific, high-frequency gaze synthesis. EyeMakeYou generates 5-s, 1000-Hz bivariate gaze-velocity sequences from an identity-removed reference trajectory and conditions the denoising process on an identity embedding, a task embedding, and self-reported ratings of overall difficulty, mental tiredness, and eye tiredness. Its objective combines diffusion noise prediction and identity preservation with multi-resolution spectral, drift-consistency, and event-weighted local-smoothness losses. Experiments on GazeBase show that EyeMakeYou achieves higher median spatial accuracy and greater real--synthetic similarity in the embedding feature space than the existing generative approaches, while retaining selected task-dependent associations between subjective reports and oculomotor features. These findings support conditional diffusion as a practical approach for augmenting gaze datasets for biometric and interactive applications.