Research map / Video / Video segmentation and tracking
Blind Video Temporal Consistency via Deep Video Prior
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the full DVP method instead of the strongest baseline | Blind video temporal consistency · Average Score | E_warp: 0.1791 -> 0.1755 | Helped |
| Use the full DVP method instead of the strongest baseline | Blind video temporal consistency · Average Score | F_data: 26.27 -> 26.55 | Helped |
| Add iteratively reweighted training (IRT) (ablation) | DVP · Colorization / Zhang et al., DAVIS | F_data: 29.61 -> 28.20 | Hurt |
| Add iteratively reweighted training (IRT) (ablation) | DVP · Colorization / Zhang et al., DAVIS | Perceptual temporal consistency | Helped |
Closest papers by meaning
- Single-Frame Regularization for Temporally Stable CNNs (CVPR 2019 · similarity 0.88)
- Learning Temporal Consistency for Low Light Video Enhancement From Single Images (CVPR 2021 · similarity 0.88)
- Learning Temporally Consistent Video Depth from Video Diffusion Priors (CVPR 2025 · similarity 0.87)
- Instant Video Models: Universal Adapters for Stabilizing Image-Based Networks (NeurIPS 2025 · similarity 0.86)
- Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors (CVPR 2025 · similarity 0.84)
- Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation (NeurIPS 2024 · similarity 0.84)
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