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pith:G2ILKQKC

pith:2026:G2ILKQKCGS36MI2EX46GQPUV5V
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Face Anything: 4D Face Reconstruction from Any Image Sequence

Matthias Nie{\ss}ner, Richard Shaw, Simon Giebenhain, Umut Kocasari

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

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
Portable graph bundle live · download bundle · merged state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

1 paper in Pith

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

arxiv: 2604.19702 · arxiv_version: 2604.19702v2 · doi: 10.48550/arxiv.2604.19702 · pith_short_12: G2ILKQKCGS36 · pith_short_16: G2ILKQKCGS36MI2E · pith_short_8: G2ILKQKC
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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
{
  "metadata": {
    "abstract_canon_sha256": "2e2d803d8ce73bcf9281fb4804a8e6733575ce432f92b0ae4092ca6e8931dde0",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-21T17:22:39Z",
    "title_canon_sha256": "1a0920c5b30e0bd009bdfd3a4d86b145668d6a4d3ef143dbcd94d9ac74282d99"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.19702",
    "kind": "arxiv",
    "version": 2
  }
}