Video Inpainting
43 papers with code • 4 benchmarks • 12 datasets
The goal of Video Inpainting is to fill in missing regions of a given video sequence with contents that are both spatially and temporally coherent. Video Inpainting, also known as video completion, has many real-world applications such as undesired object removal and video restoration.
Benchmarks
These leaderboards are used to track progress in Video Inpainting
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Datasets
DAVIS
YouTube-VOS 2018
How2Sign
FVI
KITTI360-EX
DEVIL
EgoHOS
VideoRemoval4K
Apolloscape Inpainting
Latest papers with no code
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists.
A New Low-Rank Learning Robust Quaternion Tensor Completion Method for Color Video Inpainting Problem and Fast Algorithms
In this paper, we present a new robust quaternion tensor completion (RQTC) model to solve this challenging problem and derive the exact recovery theory.
Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos
The analysis and use of egocentric videos for robotic tasks is made challenging by occlusion due to the hand and the visual mismatch between the human hand and a robot end-effector.
Dynamic Object Removal for Effective Slam
This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization.
One-Shot Video Inpainting
Usually, a video sequence and object segmentation masks for all frames are required as the input for this task.
Deep Stereo Video Inpainting
Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously.
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Recent learning-based video inpainting approaches have achieved considerable progress.
DeViT: Deformed Vision Transformers in Video Inpainting
This paper proposes a novel video inpainting method.
Semi-Supervised Video Inpainting with Cycle Consistency Constraints
Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively.
PS-NeRV: Patch-wise Stylized Neural Representations for Videos
We study how to represent a video with implicit neural representations (INRs).
