<p>Conversational gestures are essential for enabling natural and intuitive interactions with digital humans. However, the development and application of such gestures remain constrained by the scarcity of high-quality, scalable motion data. In this paper, we introduce a novel approach for controllable data acquisition and generation that combines human design, motion capture, and software synthesis to significantly reduce production costs, accelerate production speed, and ensure high-quality, precisely controlled training data. Building on this foundation, we propose an efficient method for synthesizing high-quality conversational gestures. Our framework integrates a motion feature extraction network extracted from a Transformer-based vector quantized variational autoencoder (VQ-VAE), and a cascaded gesture generation network based on Gate Recurrent Unit (GRU) with adversarial training. To further improve multi-modal data alignment and optimize data utilization, we introduce a dynamic replay buffering technique. Extensive experiments demonstrate the effectiveness of our methodology, showcasing its potential to advance both academic research and practical applications of conversational gestures for digital humans.</p>