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Video Inpainting
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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.
논문
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Let Your Video Listen to Your Music!
VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models
Follow-Your-Creation: Empowering 4D Creation through Video Inpainting
DreamDance: Animating Character Art via Inpainting Stable Gaussian Worlds
Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios
DiTPainter: Efficient Video Inpainting with Diffusion Transformers
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting
GaussVideoDreamer: 3D Scene Generation with Video Diffusion and Inconsistency-Aware Gaussian Splatting
VIP: Video Inpainting Pipeline for Real World Human Removal
AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations
MTV-Inpaint: Multi-Task Long Video Inpainting
SwapAnyone: Consistent and Realistic Video Synthesis for Swapping Any Person into Any Video
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models
DiffuEraser: A Diffusion Model for Video Inpainting
Bias for Action: Video Implicit Neural Representations with Bias Modulation
SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models
Elevating Flow-Guided Video Inpainting with Reference Generation
T-SVG: Text-Driven Stereoscopic Video Generation
UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts
Advanced Video Inpainting Using Optical Flow-Guided Efficient Diffusion
DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration
Generative Omnimatte: Learning to Decompose Video into Layers
Context-Aware Input Orchestration for Video Inpainting
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
Replace Anyone in Videos
Detecting Inpainted Video with Frequency Domain Insights
StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
Video Diffusion Models are Strong Video Inpainter
Depth-Aware Endoscopic Video Inpainting
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
Transformer-based Image and Video Inpainting: Current Challenges and Future Directions
Video Inpainting Localization with Contrastive Learning
Trusted Video Inpainting Localization via Deep Attentive Noise Learning
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