Pith Number

pith:FSIHDNNV

pith:2026:FSIHDNNVPY6HQIC6JMA5PWCEL4

not attested not anchored not stored refs resolved

An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories

Anuj Srivastava, Arafat Rahman, Laura E. Barnes, Shashwat Kumar

The Elastic Shape VAE uses a shape manifold to model skeleton trajectories by removing rigid motions and timing differences.

arxiv:2605.09231 v3 · 2026-05-10 · cs.CV · stat.ML

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\usepackage{pith}
\pithnumber{FSIHDNNVPY6HQIC6JMA5PWCEL4}

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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

Across both settings, ES-VAE consistently outperforms standard VAEs and a range of sequence modeling baselines, including temporal convolutional networks, transformers, and graph convolutional networks... offering improved latent representation and downstream performance compared to existing deep learning approaches.

C2weakest assumption

The TSRVF representation on Kendall's shape manifold inherently removes rigid translations, rotations, global scaling, and temporal rate variability while isolating the underlying shape dynamics without loss of task-relevant information.

C3one line summary

ES-VAE applies TSRVF representation on Kendall's shape manifold inside a VAE to generate and classify skeletal trajectories while removing rigid transformations and timing variability, showing gains over standard VAEs on gait scoring and NTU action recognition.

References

45 extracted · 45 resolved · 0 Pith anchors

[1] Stiff knee gait disorders as neuromechanical consequences of spastic hemiplegia in chronic stroke , author=. Toxins , volume=. 2023 , publisher= 2023

[2] Artificial Intelligence in Medicine , volume= 2009

[3] Journal of Exercise Rehabilitation , volume= 2019

[4] IEEE Transactions on Emerging Topics in Computing , volume= 2020

[5] Journal of Biopharmaceutical Statistics , volume= 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:43.331850Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2c9071b5b57e3c78205e4b01d7d8445f10e2b92fc611f6265f9925910db17612

Aliases

arxiv: 2605.09231 · arxiv_version: 2605.09231v3 · doi: 10.48550/arxiv.2605.09231 · pith_short_12: FSIHDNNVPY6H · pith_short_16: FSIHDNNVPY6HQIC6 · pith_short_8: FSIHDNNV

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FSIHDNNVPY6HQIC6JMA5PWCEL4 \
  | 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: 2c9071b5b57e3c78205e4b01d7d8445f10e2b92fc611f6265f9925910db17612

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c993569b4c2e691eadd5707c2237a099bd883ec652f2c719ee7534a272030c3c",
    "cross_cats_sorted": [
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-10T00:21:02Z",
    "title_canon_sha256": "c41d0f37367703c6f18e2a0c2773854889a41387381ecfbc72c0c131351e794a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.09231",
    "kind": "arxiv",
    "version": 3
  }
}