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Scalable Neural Video Representations with Learnable Positional Features

NeurIPS 2022 · Video › Video segmentation and tracking · approach: Neural video representations · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use learnable 2D keyframes and sparse 3D positional featuresNVP · UVG-HD, ~5 minutesPSNR: 33.15 ± 3.19 -> 34.57 ± 2.62Helped
Use learnable 2D keyframes and sparse 3D positional featuresNVP · UVG-HD, ~5 minutesLPIPS: 0.226 ± 0.112 -> 0.190 ± 0.100Helped
Use NVP instead of NeRV for frame interpolationVideo representations on Big Buck Bunny · Big Buck Bunny, frame interpolationPSNR: 23.05 -> 33.76Helped
Add learnable keyframes (ablation)NVP · UVG-HD, 150K iterationsPSNR: 31.21 ± 2.80 -> 38.89 ± 2.11Helped
Add sparse positional features (ablation)NVP · UVG-HD, 150K iterationsPSNR: 32.44 ± 4.48 -> 38.89 ± 2.11Helped
Add modulation (ablation)NVP · UVG-HD, 1.5K iterationsPSNR: 32.15 ± 3.08 -> 34.85 ± 2.69Helped
Add modulation (ablation)NVP · UVG-HD, 150K iterationsPSNR: 38.04 ± 2.27 -> 38.89 ± 2.11Helped
Include keyframes across spatial directions in addition to temporal keyframes (ablation)NVP · JockeyPSNRHelped
Concatenate nearby sparse positional feature codes (ablation)NVP · ReadySetGoreconstruction qualityHelped
Upsample sparse positional features (ablation)NVP · unseen video coordinatessmoothnessHelped
Upsample sparse positional features (ablation)NVP · NVP training, per iterationtime: 0.291s -> 0.469sHurt
Use NVP-M rather than NeRV-MVideo representations on Big Buck Bunny · Big Buck BunnyPSNR: 36.50 -> 36.49No clear effect

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