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

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Showing 1–50 of 130 papers

TitleStatusHype
Video Virtual Try-on with Conditional Diffusion Transformer Inpainter—0
Let Your Video Listen to Your Music!—0
VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models—0
Follow-Your-Creation: Empowering 4D Creation through Video Inpainting—0
DreamDance: Animating Character Art via Inpainting Stable Gaussian Worlds—0
Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios—0
DiTPainter: Efficient Video Inpainting with Diffusion Transformers—0
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting—0
GaussVideoDreamer: 3D Scene Generation with Video Diffusion and Inconsistency-Aware Gaussian Splatting—0
VIP: Video Inpainting Pipeline for Real World Human Removal—0
AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations—0
MTV-Inpaint: Multi-Task Long Video Inpainting—0
SwapAnyone: Consistent and Realistic Video Synthesis for Swapping Any Person into Any VideoCode2
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context ControlCode4
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
DiffuEraser: A Diffusion Model for Video InpaintingCode4
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Keyframe-Guided Creative Video Inpainting—0
HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion—0
SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models—0
T-SVG: Text-Driven Stereoscopic Video Generation—0
Elevating Flow-Guided Video Inpainting with Reference GenerationCode2
UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts—0
Advanced Video Inpainting Using Optical Flow-Guided Efficient DiffusionCode3
DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration—0
Context-Aware Input Orchestration for Video Inpainting—0
Generative Omnimatte: Learning to Decompose Video into Layers—0
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models—0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
Replace Anyone in VideosCode4
Detecting Inpainted Video with Frequency Domain Insights—0
StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular VideosCode3
Video Diffusion Models are Strong Video Inpainter—0
Depth-Aware Endoscopic Video InpaintingCode1
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix—0
Transformer-based Image and Video Inpainting: Current Challenges and Future Directions—0
Video Inpainting Localization with Contrastive LearningCode1
Trusted Video Inpainting Localization via Deep Attentive Noise LearningCode0
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR—0
Optimised ProPainter for Video Diminished Reality Inpainting—0
Semantically Consistent Video Inpainting with Conditional Diffusion Models—0
Raformer: Redundancy-Aware Transformer for Video Wire InpaintingCode1
Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection—0
Towards Online Real-Time Memory-based Video Inpainting Transformers—0
VQ-NeRV: A Vector Quantized Neural Representation for VideosCode1
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and CompatibilityCode3
Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs—0
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques—0
Expression-aware video inpainting for HMD removal in XR applicationsCode0
Lumiere: A Space-Time Diffusion Model for Video GenerationCode3
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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