Semantic Latent Motion for Portrait Video Generation

Recent advancements in portrait video generation have been noteworthy. However, existing methods rely heavily on human priors and pre-trained generation models, which may introduce unrealistic motion and lead to inefficient inference. To address these challenges, we propose Semantic Latent Motion (SeMo), a compact and expressive motion representation. Leveraging this representation, our approach achieve both high-quality visual results and efficient inference. SeMo follows an effective three-step framework: Abstraction, Reasoning, and Generation. First, in the Abstraction step, we use a carefully designed Mask Motion Encoder to compress the subject's motion state into a compact and abstract latent motion (1D token). Second, in the Reasoning step, long-term modeling and efficient reasoning are performed in this latent space to generate motion sequences. Finally, in the Generation step, the motion dynamics serve as conditional information to guide the generation model in synthesizing realistic transitions from reference frames to target frames. Thanks to the compact and descriptive nature of Semantic Latent Motion, our method enables real-time video generation with highly realistic motion. User studies demonstrate that our approach surpasses state-of-the-art models with an 81% win rate in realism. Extensive experiments further highlight its strong compression capability, reconstruction quality, and generative potential. Moreover, its fully self-supervised nature suggests promising applications in broader video generation tasks.