<p>In contemporary performance education, the Animal Exercise course is one of the core training modules for developing imitative behavior. Typically, instructors facilitate this process through guided demonstrations and task-based instruction, encouraging students to engage in both imitation and creative exploration. The pedagogical approach is therefore characterized by active student participation and a strong emphasis on experiential, practice-oriented learning. However, assessment in Animal Exercise courses still relies primarily on instructors’ subjective judgment, resulting in inconsistent and non-standardized evaluations. This hinders students’ ability to identify skill deficiencies and improve their course performance. To address this challenge, we propose a quantitative framework for evaluating imitative behavior using pose estimation, termed Human Pose Estimation–Imitative Behavior Analysis (HPE-IBA). Using this framework, we employ a standard RGB camera to collect motion data from both students and gorillas, extract three-dimensional joint coordinates, and compute dynamic joint angles with MediaPipe. We then apply correlation analysis to identify weakly correlated features and core joints, followed by two-way ANOVA to examine the effects of training status and gender on students’ imitation performance. Analysis of chest-beating and walking imitation reveals a statistically significant interaction between training status and gender (p&lt; 0.01), primarily reflected in joint patterns such as the right elbow and right knee. The proposed framework not only enhances the application of pose estimation in acting education but also provides a foundation for broader applications in performance-based motion analysis.</p>