The provided research paper introduces KDTalker, a novel method for generating realistic audio-driven talking portraits by combining implicit 3D keypoints with a spatiotemporal diffusion model. This framework addresses limitations in existing techniques by achieving high lip synchronization accuracy and diverse head poses while maintaining computational efficiency. KDTalker leverages unsupervised learning of adaptable facial keypoints and a custom attention mechanism to ensure temporally consistent and expressive animations from a single image and audio. Experimental results demonstrate KDTalker's superior performance compared to state-of-the-art methods in terms of visual quality, motion diversity, and synchronization. The paper also includes ablation studies that validate the contributions of different components of the proposed framework.