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Video-to-video translation is more difficult than image-to-image translationdue to the temporal consistency problem that, if unaddressed, leads todistracting flickering effects. Although video models designed from scratchproduce temporally consistent results, training them to match the vast visualknowledge captured by image models requires an intractable number of videos. Tocombine the benefits of image and video models, we propose an image-to-videomodel transfer method called Hyperconsistency (HyperCon) that transforms anywell-trained image model into a temporally consistent video model withoutfine-tuning. HyperCon works by translating a temporally interpolated videoframe-wise and then aggregating over temporally localized windows on theinterpolated video. It handles both masked and unmasked inputs, enablingsupport for even more video-to-video translation tasks than priorimage-to-video model transfer techniques. We demonstrate HyperCon on videostyle transfer and inpainting, where it performs favorably compared to priorstate-of-the-art methods without training on a single stylized or incompletevideo. Our project website is available athttps://ryanszeto.com/projects/hypercon .

HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks