EmWorld: Emotion World Model with Latent State Evolution for Scenario-Incremental Dynamic Facial Expression Recognition

Abstract

Dynamic Facial Expression Recognition (DFER) models the temporal evolution of facial expressions in videos. In real-world scenarios, changing scenarios distort expression trajectories, challenging existing methods. Most current approaches address this via passive feature alignment or domain-incremental learning but do not explicitly model scenario evolution, limiting their ability to capture expression dynamics under scenario-incremental changes. To address this, we propose **EmWorld**, an emotion world model for DFER that explicitly models latent emotion state evolution under scenario variations. Specifically, **EmWorld** formulates scenario-incremental DFER as a progressive Bayesian inference problem over latent world states with dual temporal scales. Slow-timescale component (**STS**) models scenario evolution using stochastic evolutionary priors, capturing long-term scenario effects and providing proactive guidance in new scenarios. Fast-timescale component (**FTS**) models frame-level expression dynamics with temporally consistent latent transitions, decoupling expression dynamics from scenario influences. By jointly inferring latent states at both timescales, EmWorld shifts DFER from a passive feature discrimination to active probabilistic state inference under evolving scenarios. Experiments on FERV39k, DFEW, and MAFW demonstrate that EmWorld consistently outperforms state-of-the-art methods, achieving up to 3.84\% improvement while exhibiting strong cross-scenario stability and long-term robustness.