In recent years, facial animation generation technology has become one of the directions that has attracted much attention in the field of computer vision, and the generated facial animation has made different degrees of progress in the quality of lip-sync and the degree of emotional controllability. However, existing studies often unavoidably affect the lip movements when generating facial expressions. In order to improve the accuracy of lip-sync, generative adversarial networksmethod was used to investigate multi-modal driven emotion controllable model. The model consisted of two deep learning networks connected in series to generate high-quality lip-sync and emotionally controllable facial animation driven by three modal data, namely audio, text, and emotional portrait images, after inputting any expressionless target portrait image. In this process, the text features played a role in assisting the prediction of lip movements for the audio, and they were the key step in this technical study to improve the quality of lip-sync. The results show that on the MEAD dataset, compared to existing facial animation generation methods MakeItTalk and emotion aware motion model (EAMM), the proposed model reduces the lip-landmark distance (L-LD) by 5.93% and 33.52%, and the face-landmark distance (F-LD) by 7.00% and 8.79%, respectively. It is concluded that the proposed model improves the accuracy of lip synchronization and emotion control.