Align-and-Attend Network for Globally and Locally Coherent Video Inpainting

We propose a novel feed-forward network for video inpainting. We use a set of sampled video frames as the reference to take visible contents to fill the hole of a target frame. Our video inpainting network consists of two stages. The first stage is an alignment module that uses computed homographies between the reference frames and the target frame. The visible patches are then aggregated based on the frame similarity to fill in the target holes roughly. The second stage is a non-local attention module that matches the generated patches with known reference patches (in space and time) to refine the previous global alignment stage. Both stages consist of large spatial-temporal window size for the reference and thus enable modeling long-range correlations between distant information and the hole regions. Therefore, even challenging scenes with large or slowly moving holes can be handled, which have been hardly modeled by existing flow-based approach. Our network is also designed with a recurrent propagation stream to encourage temporal consistency in video results. Experiments on video object removal demonstrate that our method inpaints the holes with globally and locally coherent contents

Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning

Dynamic objects have a significant impact on the robot's perception of the environment which degrades the performance of essential tasks such as localization and mapping. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We propose the novel geometry-aware DynaFill architecture that follows a coarse-to-fine topology and incorporates our gated recurrent feedback mechanism to adaptively fuse information from previous timesteps. We optimize our architecture using adversarial training to synthesize fine realistic textures which enables it to hallucinate color and depth structure in occluded regions online in a spatially and temporally coherent manner, without relying on future frame information. Casting our inpainting problem as an image-to-image translation task, our model also corrects regions correlated with the presence of dynamic objects in the scene, such as shadows or reflections. We introduce a large-scale hyperrealistic dataset with RGB-D images, semantic segmentation labels, camera poses as well as groundtruth RGB-D information of occluded regions. Extensive quantitative and qualitative evaluations show that our approach achieves state-of-the-art performance, even in challenging weather conditions. Furthermore, we present results for retrieval-based visual localization with the synthesized images that demonstrate the utility of our approach.Comment: Dataset, code and models are available at http://rl.uni-freiburg.de/research/rgbd-inpaintin