Learning Joint Spatial-Temporal Transformations for Video Inpainting

High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can suffer from inconsistent attention results along spatial and temporal dimensions, which often leads to blurriness and temporal artifacts in videos. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks. Demo videos are available at https://github.com/researchmm/STTN.Comment: Accepted by ECCV202

FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer

Limited by hardware cost and system size, camera's Field-of-View (FoV) is not always satisfactory. However, from a spatio-temporal perspective, information beyond the camera's physical FoV is off-the-shelf and can actually be obtained "for free" from the past. In this paper, we propose a novel task termed Beyond-FoV Estimation, aiming to exploit past visual cues and bidirectional break through the physical FoV of a camera. We put forward a FlowLens architecture to expand the FoV by achieving feature propagation explicitly by optical flow and implicitly by a novel clip-recurrent transformer, which has two appealing features: 1) FlowLens comprises a newly proposed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated in the temporal dimension. 2) A multi-branch Mix Fusion Feed Forward Network (MixF3N) is integrated to enhance the spatially-precise flow of local features. To foster training and evaluation, we establish KITTI360-EX, a dataset for outer- and inner FoV expansion. Extensive experiments on both video inpainting and beyond-FoV estimation tasks show that FlowLens achieves state-of-the-art performance. Code will be made publicly available at https://github.com/MasterHow/FlowLens.Comment: Code will be made publicly available at https://github.com/MasterHow/FlowLen

FVIFormer: flow-guided global-local aggregation transformer network for video inpainting

Video inpainting has been extensively used in recent years. Established works usually utilise the similarity between the missing region and its surrounding features to inpaint in the visually damaged content in a multi-stage manner. However, due to the complexity of the video content, it may result in the destruction of structural information of objects within the video. In addition to this, the presence of moving objects in the damaged regions of the video can further increase the difficulty of this work. To address these issues, we propose a flow-guided global-Local aggregation Transformer network for video inpainting. First, we use a pre-trained optical flow complementation network to repair the defective optical flow of video frames. Then, we propose a content inpainting module, which use the complete optical flow as a guide, and propagate the global content across the video frames using efficient temporal and spacial Transformer to inpaint in the corrupted regions of the video. Finally, we propose a structural rectification module to enhance the coherence of content around the missing regions via combining the extracted local and global features. In addition, considering the efficiency of the overall framework, we also optimized the self-attention mechanism to improve the speed of training and testing via depth-wise separable encoding. We validate the effectiveness of our method on the YouTube-VOS and DAVIS video datasets. Extensive experiment results demonstrate the effectiveness of our approach in edge-complementing video content that has undergone stabilisation algorithms

DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality

Diminished reality (DR) refers to the removal of real objects from the environment by virtually replacing them with their background. Modern DR frameworks use inpainting to hallucinate unobserved regions. While recent deep learning-based inpainting is promising, the DR use case is complicated by the need to generate coherent structure and 3D geometry (i.e., depth), in particular for advanced applications, such as 3D scene editing. In this paper, we propose DeepDR, a first RGB-D inpainting framework fulfilling all requirements of DR: Plausible image and geometry inpainting with coherent structure, running at real-time frame rates, with minimal temporal artifacts. Our structure-aware generative network allows us to explicitly condition color and depth outputs on the scene semantics, overcoming the difficulty of reconstructing sharp and consistent boundaries in regions with complex backgrounds. Experimental results show that the proposed framework can outperform related work qualitatively and quantitatively.Comment: 11 pages, 8 figures + 13 pages, 10 figures supplementary. Accepted at 3DV 202