pith:ITMISEQZ
You Only Landmark Once: Lightweight U-Net Face Super Resolution with YOLO-World Landmark Heatmaps
A lightweight U-Net reconstructs 128x128 faces from 16x16 inputs by weighting its loss with YOLO-World landmark heatmaps.
arxiv:2605.14166 v1 · 2026-05-13 · cs.CV
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Claims
Experiments on the aligned CelebA dataset demonstrate that the proposed loss consistently improves quantitative metrics and produces sharper, more realistic reconstructions.
YOLO-World heatmaps generated directly from severely degraded 16x16 inputs remain accurate enough to serve as reliable spatial weights for the reconstruction loss without introducing misalignment artifacts.
Lightweight U-Net for 8x face super-resolution uses YOLO-World landmark heatmaps as spatial loss weights to improve reconstruction on CelebA without extra networks or adversarial training.
References
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| First computed | 2026-05-17T23:39:11.408641Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
44d8891219f9628192ced1d267aac6ccbe16ae0c7d1c04466f4de20083da3fb5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/ITMISEQZ7FRIDEWO2HJGPKWGZS \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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