The September 2025 paper introduces MotionRAG, a novel retrieval-augmented framework designed to enhance motion realism in image-to-video generation. The central challenge addressed is the difficulty diffusion models face in generating videos with physically plausible and coherent motion. MotionRAG solves this by using a retrieval pipeline to adapt high-level motion priors from relevant reference videos through its core component, the Context-Aware Motion Adaptation (CAMA) module. This approach formulates motion transfer as an in-context learning problem, allowing the system to generalize to new domains with negligible computational overhead and without requiring fine-tuning of the base generation models. Experimental results demonstrate that MotionRAG significantly improves motion quality across various state-of-the-art models and datasets, including specialized domains, by effectively guiding video generation with real-world motion patterns. Source: https://arxiv.org/pdf/2509.26391