1. We have developed a video-based facial keypoint detection algorithm, which includes data collection, cleaning, training, optimization, and compression. The algorithm has been implemented in Python. Additionally, we have deployed the algorithm using C++.2. We have successfully developed a real-time facial capture algorithm based on videos, achieving high-performance deployment. The algorithm runs purely on CPU and achieves a processing speed of 120 frames per second.3. Our research focuses on real-time 3D facial reconstruction using depth cameras. We have developed a low-computational fusion algorithm for point cloud fusion and non-rigid registration.4. Our software, named Avatary, encompasses real-time facial capture modules, including model-driven tracking and retargeting functionalities. The software has been released, allowing users to perform real-time facial capture and tracking tasks.5. We have conducted research on finite element-based facial muscle simulation. The simulation includes hyperelastic material constitutive modeling, implicit integration, and considerations for constraints, collisions, and muscle contraction.1. 本研究针对视频中的人脸关键点检测算法进行了开发，涵盖了数据采集、清理、训练、调优和压缩等关键步骤，并采用Python编程语言实现。此外，还进行了基于C++的算法部署。2. 我们成功地研发了一种基于视频的实时面部捕捉算法，并实现了高性能的部署。该算法在仅使用CPU的情况下，能够达到每秒120帧的处理速度。3. 本研究提出了一种基于深度摄像头的实时人脸三维重建方法，该方法采用了一种低算力的融合算法，实现了点云融合和非刚性配准的功能。4. 我们所开发的软件名为Avatary，其中包括实时人脸面部捕捉模块，涵盖了模型驱动跟踪（tracking）和模型重定向（retargeter）功能。该软件已经成功发布，用户可以利用它进行实时面部捕捉和驱动跟踪任务。5.本研究探索了有限元面部肌肉模拟仿真，采用超弹性材料本构、隐式积分，并考虑了约束、碰撞和肌肉收缩等因素。这一仿真方法能够模拟面部肌肉的行为和反应。