VideoJAM fixes AI video's physics problem with joint motion representations
Researchers just introduced VideoJAM, a framework designed to tackle AI video's persistent struggle with real-world physics and motion coherence. By training models on joint appearance-motion representations and using internal motion cues to steer generation during inference, it plugs into existing architectures and outperforms proprietary baselines like Sora on complex motion.