In the contemporary timeframe, 3D human modeling has become increasingly important due to broad spectrum in leveraging utilities. It can create realistic representations of human anatomy, which is useful in healthcare for training, simulation, and surgical planning. The entertainment industry also uses 3D human modeling to create digital characters, while the fashion and retail industry uses it for virtual fitting rooms and personalized clothing recommendations. With advancing technology and new applications emerging, the relevance of 3D human modeling is expected to continue growing. The following research paper investigates the combination of motion capture and 3D facial reconstruction techniques for enhanced character animation. We advocate a blended method that integrates the strengths of both techniques, creating a more lifelike and expressive animation. Our method combines a deep learning-based 3D facial reconstruction algorithm with motion capture data to produce high-quality facial animation. We evaluate our method by comparing it to traditional animation methods and show that our approach produces more realistic and natural facial expressions. Our results demonstrate the potential for combining motion capture and 3D facial reconstruction techniques to enhance character animation in the entertainment industry and beyond.