This paper investigates the problem of human motion sequence generation conditioned on action labels. Generating realistic motion sequences is of great significance for enhancing the performance of vision-related tasks such as action recognition and pose analysis. However, existing methods still suffer from limitations in generation accuracy and effective utilization of conditional information. In particular, the VQ-VAE architecture has restricted capability in modeling global features of motion sequences and insufficient exploitation of conditional cues. To address these issues, we propose a novel VQ-VAE architecture that learns high-quality discrete representations. The proposed framework leverages the complementary strengths of Transformers and CNNs, while integrating conditional information effectively during the decoding stage to improve generation quality. Furthermore, we introduce a generative pre-trained autoregressive model to predict discrete codebook indices, thereby further enhancing the quality of sequence generation. By integrating these two modules, we develop a generative model that produces human motion sequences conditioned on action labels, and conduct systematic evaluations on the HumanAct12 and UESTC datasets. Experimental results demonstrate that our method significantly outperforms several existing approaches in generation quality, validating the competitiveness and potential of the proposed VQ-VAE framework for human motion sequence generation.