Pith Number

pith:NYFEGIJW

pith:2026:NYFEGIJWUS3VTYEL7FY73SYBZV

not attested not anchored not stored refs resolved

Generative Motion In-betweening by Diffusion over Continuous Implicit Representations

Edmond S. L. Ho, Paul Henderson, Shiyu Fan

Latent diffusion on implicit neural representations generates plausible motions from sparse keyframes.

arxiv:2605.12778 v1 · 2026-05-12 · cs.GR · cs.CV

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\pithnumber{NYFEGIJWUS3VTYEL7FY73SYBZV}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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 establishing a mapping between INR and sparse spatial or temporal information within latent diffusion, our model can sample the INR parameters from extremely sparse and ambiguous keyframe data and reconstruct plausible and smooth motions from the manifold.

C2weakest assumption

That a learned mapping from sparse keyframes into the latent space of an INR-based diffusion model will reliably produce motions that remain both accurate at the keyframes and continuous in between without additional post-processing or constraints.

C3one line summary

A latent diffusion model over continuous implicit neural representations samples INR parameters from sparse keyframes to reconstruct plausible, smooth, and diverse motions while preserving keyframe accuracy.

References

49 extracted · 49 resolved · 1 Pith anchors

[1] F. G. Harvey, M. Yurick, D. Nowrouzezahrai, and C. Pal, “Robust motion in-betweening,”ACM Trans. Graph., vol. 39, no. 4, 2020 2020

[2] Denoising diffusion probabilistic models 2020

[3] Human motion diffusion model, 2023

[4] Motiondiffuse: Text-driven human motion generation with diffusion model, 2024

[5] Omnicontrol: Control any joint at any time for human motion generation, 2024

Receipt and verification

First computed	2026-05-18T03:09:13.184693Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6e0a432136a4b759e08bf971fdcb01cd7f60d02dbcc621a6406dcb04119085bc

Aliases

arxiv: 2605.12778 · arxiv_version: 2605.12778v1 · doi: 10.48550/arxiv.2605.12778 · pith_short_12: NYFEGIJWUS3V · pith_short_16: NYFEGIJWUS3VTYEL · pith_short_8: NYFEGIJW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NYFEGIJWUS3VTYEL7FY73SYBZV \
  | 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: 6e0a432136a4b759e08bf971fdcb01cd7f60d02dbcc621a6406dcb04119085bc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "013b8abd45c03ca4b521e045b576c84fa9d0c1cec995a94c334de3a9e65a704b",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.GR",
    "submitted_at": "2026-05-12T21:48:14Z",
    "title_canon_sha256": "90ee90f423f73b187d26134fa4b7378637779f5c252031603a843df44ebf662a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12778",
    "kind": "arxiv",
    "version": 1
  }
}