Botulinum toxin (Botox) injections are the gold standard for managing facial asymmetry and aesthetic rejuvenation, yet determining the optimal dosage remains largely intuitive, often leading to suboptimal outcomes. We propose a localized latent editing framework that simulates Botulinum Toxin injection effects for injection planning through dose-response modeling. Our key contribution is a Region-Specific Latent Axis Discovery method that learns localized muscle relaxation trajectories in StyleGAN2's latent space, enabling precise control over specific facial regions without global side effects. By correlating these localized latent trajectories with injected toxin units, we learn a predictive dose-response model. We rigorously compare two approaches: direct metric regression versus image-based generative simulation on a clinical dataset of N=360 images from 46 patients. On a hold-out test set, our framework demonstrates moderate-to-strong structural correlations for geometric asymmetry metrics, confirming that the generative model correctly captures the direction of morphological changes. While biological variability limits absolute precision, we introduce a hybrid "Human-in-the-Loop" workflow where clinicians interactively refine simulations, bridging the gap between pathological reconstruction and cosmetic planning.

翻译：肉毒毒素（Botox）注射是治疗面部不对称和实现美学年轻化的黄金标准，然而确定最佳剂量在很大程度上仍依赖经验直觉，常导致次优结果。我们提出一种局部潜在编辑框架，通过剂量-反应建模模拟肉毒毒素注射效果以辅助注射规划。我们的核心贡献是提出区域特异性潜在轴发现方法，该方法在StyleGAN2的潜在空间中学习局部肌肉松弛轨迹，从而实现对特定面部区域的精确控制，且不产生全局副作用。通过将这些局部潜在轨迹与注射毒素单位相关联，我们建立了预测性剂量-反应模型。我们在包含46名患者N=360张图像的临床数据集上，严格比较了两种方法：直接度量回归与基于图像的生成式模拟。在独立测试集上，本框架在几何不对称度量指标上表现出中等至较强的结构相关性，证实生成模型能准确捕捉形态变化方向。虽然生物变异性限制了绝对精度，但我们引入混合式"人在回路"工作流程，使临床医生能交互优化模拟结果，从而弥合病理重建与美容规划之间的鸿沟。