ReVRA: Reflective Video Restoration Agent with Self-Corrective Dynamic Pipelines

Degradations in real-world user-generated videos are inherently complex and multifaceted, making monolithic restoration tools struggle to decouple and resolve multiple, overlapping artifacts. While recent agentic restoration systems offer greater flexibility by chaining specialized restoration tools into pipelines, they suffer from three limitations: planning relies solely on the input video and static tool priors, making it difficult to select appropriate toolboxes; execution follows a fixed pipeline that cannot adapt to unexpected artifacts introduced by tools; and evaluation relies on absolute quality scores that poorly align with human perception. We propose ReVRA, a novel Reflective Video Restoration Agent that constructs adaptive restoration tool sequences through dynamic self-correction. ReVRA decomposes complex restoration into self-corrective stages that re-examine the current video state to adapt tool usage to unexpected artifacts. To improve planning, ReVRA replaces one-shot prediction with toolbox decisions guided by instance-aware evolving memory and verified by execution outcomes, which also refine the memory to provide increasingly accurate priors. Moreover, ReVRA evaluates restoration results via relative comparisons, better aligning with the human preference selection process. Experiments across various benchmarks spanning synthetic and real-world degradation scenarios demonstrate that ReVRA achieves superior restoration quality across a wide spectrum of complex degradations, validating the effectiveness of the reflective agentic framework for real-world video restoration.