Abstract

Recent advances in video diffusion models have unlocked new potential for realistic audio-driven talking video generation. However, maintaining long-term identity consistency, achieving seamless lip-audio synchronization, and producing natural, audio-aligned expressions in generated talking videos remain significant challenges. To address these challenges, we propose Memory-guided EMOtion-aware diffusion (MEMO), an end-to-end audio-driven portrait animation approach to generate identity-consistent and expressive talking videos. Our approach is built around two key modules: (1) a memory-guided temporal module, which enhances long-term identity consistency and motion smoothness by developing causal motion memory to store information from an extended past context to guide temporal modeling; and (2) an emotion-aware audio module, which replaces traditional cross attention with multi-modal attention to enhance audio-video interaction, while detecting emotions from audio to refine facial expressions via emotion-adaptive layer norm. Extensive quantitative and qualitative results demonstrate that MEMO generates more realistic talking videos across diverse image and audio types, outperforming state-of-the-art methods in overall quality, lip-audio synchronization, identity consistency, and expression-audio alignment. Our model and video demos are available at https://memoavatar.github.io.