Artificial Intelligence Generated Content (AIGC), as a computer technology mainly characterized by intelligent content generation, has caused significant changes in film and television performances and creations, and has greatly broadened the creation and development space of film and television performances. In this paper, we use motion capture technology to obtain the character movement data in film and television performances, and combine it with the skeletal motion data generation algorithm to realize the mapping of skeletal motion data. Using ResNet-122 as the backbone network, a 3D action pose estimation model is constructed by combining multi-view and multi-feature fusion networks. Based on the 3D action pose estimation sequence, the character animation generation model is constructed by combining GAN and action detail attention mechanism, and the action detail feature loss function is designed to improve the generalization ability of the animation generation model. In order to verify the effectiveness of the above method, data analysis is carried out through simulation verification. The average value of PCP3D index of the 3D action pose estimation model is 98.37, which is 0.28 percentage points higher than the sub-optimal model, and the average joint position error is only 16.07 mm. The animation generation model combining GAN and the action detail attention mechanism has the values of animation generation diversity and richness index of 5.104 and 3.997, respectively, and the animation generation diversity and richness indexes of the animation generation model combining GAN and the action detail attention mechanism are 5.104 and 3.997, respectively. 3Ds MAX software can map the generated animation sequences into the virtual space, providing assistance for optimizing the motion design of film and television performances.