pith:G2ILKQKC
Face Anything: 4D Face Reconstruction from Any Image Sequence
Canonical facial point prediction unifies depth estimation, dense 3D geometry, and point tracking for 4D face reconstruction from single-view sequences.
arxiv:2604.19702 v2 · 2026-04-21 · cs.CV
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\pithnumber{G2ILKQKCGS36MI2EX46GQPUV5V}
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Record completeness
Claims
By jointly predicting depth and canonical coordinates, our method enables accurate depth estimation, temporally stable reconstruction, dense 3D geometry, and robust facial point tracking within a single architecture.
That multi-view geometry data can be reliably non-rigidly warped into a shared canonical space to train a model that then generalizes to arbitrary single-view image sequences without additional constraints or post-processing.
A single transformer model jointly predicts depth and normalized canonical coordinates to deliver state-of-the-art 4D facial geometry and tracking with 3x lower correspondence error and 16% better depth accuracy.
Cited by
Receipt and verification
| First computed | 2026-06-30T02:17:21.348221Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3690b5414234b7e62344bf3c683e95ed4bf83de142aa36da3f8e8524c9f85cc9
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/G2ILKQKCGS36MI2EX46GQPUV5V \
| 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())"
# expect: 3690b5414234b7e62344bf3c683e95ed4bf83de142aa36da3f8e8524c9f85cc9
Canonical record JSON
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