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Quantitative evaluation has increased dramatically among recent videoinpainting work, but the video and mask content used to gauge performance hasreceived relatively little attention. Although attributes such as camera andbackground scene motion inherently change the difficulty of the task and affectmethods differently, existing evaluation schemes fail to control for them,thereby providing minimal insight into inpainting failure modes. To addressthis gap, we propose the Diagnostic Evaluation of Video Inpainting onLandscapes (DEVIL) benchmark, which consists of two contributions: (i) a noveldataset of videos and masks labeled according to several key inpainting failuremodes, and (ii) an evaluation scheme that samples slices of the datasetcharacterized by a fixed content attribute, and scores performance on eachslice according to reconstruction, realism, and temporal consistency quality.By revealing systematic changes in performance induced by particularcharacteristics of the input content, our challenging benchmark enables moreinsightful analysis into video inpainting methods and serves as an invaluablediagnostic tool for the field. Our code and data are available athttps://github.com/MichiganCOG/devil .

The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting