Conventional video inpainting is neither object-oriented nor occlusion-aware,
making it liable to obvious artifacts when large occluded object regions are
inpainted. This paper presents occlusion-aware video object inpainting, which
recovers both the complete shape and appearance for occluded objects in videos
given their visible mask segmentation.
To facilitate this new research, we construct the first large-scale video
object inpainting benchmark YouTube-VOI to provide realistic occlusion
scenarios with both occluded and visible object masks available. Our technical
contribution VOIN jointly performs video object shape completion and occluded
texture generation. In particular, the shape completion module models
long-range object coherence while the flow completion module recovers accurate
flow with sharp motion boundary, for propagating temporally-consistent texture
to the same moving object across frames. For more realistic results, VOIN is
optimized using both T-PatchGAN and a new spatio-temporal attention-based
multi-class discriminator.
Finally, we compare VOIN and strong baselines on YouTube-VOI. Experimental
results clearly demonstrate the efficacy of our method including inpainting
complex and dynamic objects. VOIN degrades gracefully with inaccurate input
visible mask.

通过建立大规模视频目标修复基准数据集与新的 VOIN 技术将视频对象形状完成和遮挡纹理生成相结合，本论文提出了一种面向对象和遮挡感知的视频修复方法，致力于准确还原大尺度遮挡对象区域的形状和外观。