SOTAVerified

Video Inpainting

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.

Source: Deep Flow-Guided Video Inpainting

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 130 papers

TitleStatusHype
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting—0
Tensor Completion via Tensor Networks with a Tucker Wrapper—0
InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes—0
Short-Term and Long-Term Context Aggregation Network for Video Inpainting—0
Flow-edge Guided Video CompletionCode2
Proposal-based Video Completion—0
Learning Joint Spatial-Temporal Transformations for Video InpaintingCode1
DVI: Depth Guided Video Inpainting for Autonomous DrivingCode1
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks—0
AutoRemover: Automatic Object Removal for Autonomous Driving VideosCode0
An Internal Learning Approach to Video InpaintingCode0
Copy-and-Paste Networks for Deep Video InpaintingCode0
Onion-Peel Networks for Deep Video CompletionCode0
Faster Unsupervised Semantic Inpainting: A GAN Based Approach—0
Learnable Gated Temporal Shift Module for Deep Video InpaintingCode0
Align-and-Attend Network for Globally and Locally Coherent Video Inpainting—0
Deep Flow-Guided Video InpaintingCode0
Deep Blind Video Decaptioning by Temporal Aggregation and RecurrenceCode0
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference—0
Deep Video InpaintingCode0
Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGANCode0
VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal—0
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details—0
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor TrainsCode0
A Temporally-Aware Interpolation Network for Video Frame InpaintingCode0
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All datasetsDAVISYouTube-VOS 2018HQVI (240p)HQVI (480p)HQVI (2K)YouTube-VOSHow2Sign

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DMTPSNR33.82—Unverified
2E2FGVIPSNR33.01—Unverified
3FuseFormerPSNR32.54—Unverified
4FGVCPSNR30.8—Unverified
5STTNPSNR30.67—Unverified
6CAPPSNR30.28—Unverified
7VINetPSNR28.96—Unverified
8DFVIPSNR28.81—Unverified
9LGTSMPSNR28.57—Unverified
10FGT++LPIPS (object)0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1ProPainterPSNR34.43—Unverified
2DMTPSNR34.27—Unverified
3E2FGVIPSNR33.71—Unverified
4FuseFormerPSNR33.29—Unverified
5STTNPSNR32.34—Unverified
6CAPPSNR31.58—Unverified
7LGTSMPSNR29.74—Unverified
8FGVCPSNR29.67—Unverified
9VINetPSNR29.2—Unverified
10DFVIPSNR29.16—Unverified
#ModelMetricClaimedVerifiedStatus
1STTNLPIPS0.05—Unverified
2FuseFormerLPIPS0.05—Unverified
3FGVCLPIPS0.04—Unverified
4E2FGVILPIPS0.04—Unverified
5RGVI w/o Ref.LPIPS0.04—Unverified
6ProPainterLPIPS0.04—Unverified
7RGVILPIPS0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ProPainterLPIPS0.05—Unverified
2RGVI w/o Ref.LPIPS0.04—Unverified
3FGVCLPIPS0.04—Unverified
4RGVILPIPS0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1RGVI w/o Ref.LPIPS0.04—Unverified
2RGVILPIPS0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1FGT++LPIPS0.03—Unverified
2FGT++*LPIPS0.02—Unverified
#ModelMetricClaimedVerifiedStatus
1INR-VL1 error4.51—Unverified