Research map / Video / Video segmentation and tracking
Deep Video Inpainting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add recurrent feedback and ConvLSTM temporal memory to VINet (ablation) | VINet · Sintel frames with DAVIS masks | flow warping error: 0.0383 -> 0.0015 | Helped |
| Add recurrent feedback and ConvLSTM temporal memory to VINet (ablation) | VINet · DAVIS frames with DAVIS masks | FID: 0.0073 -> 0.0046 | Helped |
Closest papers by meaning
- Video Inpainting by Jointly Learning Temporal Structure and Spatial Details (AAAI 2019 · similarity 0.92)
- Keyframe-Guided Creative Video Inpainting (CVPR 2025 · similarity 0.87)
- Video Diffusion Models Are Strong Video Inpainter (AAAI 2025 · similarity 0.86)
- Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model (NeurIPS 2024 · similarity 0.85)
- Elevating Flow-Guided Video Inpainting with Reference Generation (AAAI 2025 · similarity 0.85)
- Towards Language-Driven Video Inpainting via Multimodal Large Language Models (CVPR 2024 · similarity 0.85)
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