Tuning-free Visual Effect Transfer across Videos

We present RefVFX, a new framework that transfers complextemporal effects from a reference video onto a target video or image ina feed-forward manner. While existing methods excel at prompt-basedor keyframe-conditioned editing, they struggle with dynamic temporaleffects such as dynamic lighting changes or character transformations,which are difficult to describe via text or static conditions. Transferringa video effect is challenging, as the model must integrate the new tem-poral dynamics with the input video’s existing motion and appearance.To address this, we introduce a large-scale dataset of triplets, where eachtriplet consists of a reference effect video, an input image or video, and acorresponding output video depicting the transferred effect. Creating thisdata is non-trivial, especially the video-to-video effect triplets, which donot exist naturally. To generate these, we propose a scalable automatedpipeline that creates high-quality paired videos designed to preserve theinput’s motion and structure while transforming it based on some fixed,repeatable effect. We then augment this data with image-to-video effectsderived from LoRA adapters and code-based temporal effects generatedthrough programmatic composition. Building on our new dataset, wetrain our reference-conditioned model using recent text-to-video back-bones. Experimental results demonstrate that RefVFX produces visuallyconsistent and temporally coherent edits, generalizes across unseen effectcategories, and outperforms prompt-only baselines in both quantitativemetrics and human preference. See our website at this URL