LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation
Current video generation models often fail to maintain physical and motion consistency, limiting their reliability as world simulators. This work proposes a self-supervised latent motion prior that leverages only unlabeled videos to model inter-frame motion dynamics within the latent space of diffusion models, without requiring external supervision. By incorporating two lightweight components—a macroscopic motion drift loss and a microscopic motion field guidance—the method effectively enhances the physical plausibility of generated videos. Evaluated on VideoPhy and VideoPhy2 benchmarks, the approach outperforms baselines that rely on external supervisory signals, while maintaining competitive overall generation quality on VBench and achieving significant improvements in motion-related metrics.