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 51–100 of 130 papers

TitleStatusHype
Towards Language-Driven Video Inpainting via Multimodal Large Language ModelsCode2
Deep Learning-based Image and Video Inpainting: A Survey—0
AVID: Any-Length Video Inpainting with Diffusion ModelCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Flow-Guided Diffusion for Video InpaintingCode2
Infusion: internal diffusion for inpainting of dynamic textures and complex motionCode1
Deep Video Inpainting Guided by Audio-Visual Self-SupervisionCode0
UVL2: A Unified Framework for Video Tampering Localization—0
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and MethodCode1
ProPainter: Improving Propagation and Transformer for Video InpaintingCode5
UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery LocalizationCode1
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation—0
Deficiency-Aware Masked Transformer for Video InpaintingCode1
A New Low-Rank Learning Robust Quaternion Tensor Completion Method for Color Video Inpainting Problem and Fast Algorithms—0
Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos—0
HNeRV: A Hybrid Neural Representation for VideosCode1
Dynamic Object Removal for Effective Slam—0
One-Shot Video Inpainting—0
Exploiting Optical Flow Guidance for Transformer-Based Video InpaintingCode2
CIRI: Curricular Inactivation for Residue-aware One-shot Video InpaintingCode0
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer—0
Deep Stereo Video Inpainting—0
Video Instance Shadow Detection Under the Sun and SkyCode1
Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent TransformerCode1
INR-V: A Continuous Representation Space for Video-based Generative TasksCode1
Scalable Neural Video Representations with Learnable Positional FeaturesCode1
DeViT: Deformed Vision Transformers in Video Inpainting—0
Flow-Guided Transformer for Video InpaintingCode2
Semi-Supervised Video Inpainting with Cycle Consistency Constraints—0
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and ApplicationsCode1
PS-NeRV: Patch-wise Stylized Neural Representations for Videos—0
Error Compensation Framework for Flow-Guided Video Inpainting—0
Towards Unified Keyframe Propagation ModelsCode2
Towards An End-to-End Framework for Flow-Guided Video InpaintingCode3
Inertia-Guided Flow Completion and Style Fusion for Video InpaintingCode1
DLFormer: Discrete Latent Transformer for Video Inpainting—0
Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake VideosCode0
Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting—0
Diverse Generation from a Single Video Made PossibleCode1
FuseFormer: Fusing Fine-Grained Information in Transformers for Video InpaintingCode1
Deep Face Video Inpainting via UV Mapping—0
Flow-Guided Video Inpainting with Scene Templates—0
Occlusion-Aware Video Object Inpainting—0
Internal Video Inpainting by Implicit Long-range PropagationCode1
The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video InpaintingCode1
Decoupled Spatial-Temporal Transformer for Video InpaintingCode1
Progressive Temporal Feature Alignment Network for Video InpaintingCode1
Deep Video Inpainting Detection—0
Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information—0
Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection—0
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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