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