Pith Number

pith:LWT5KWEG

pith:2026:LWT5KWEGTTPUELAD4SSXK45EWB

not attested not anchored not stored refs resolved

MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation

Bin Xia, Dongxia Liu, Jiancheng Zhang, Jie Ma, Jin Li, Jun Liang, Nisha Huang, Wenming Yang, Xiaochen Yang, Zhehan Kan

MoZoo generates high-fidelity animal fur and muscle videos directly from coarse meshes using video diffusion.

arxiv:2605.13857 v1 · 2026-04-08 · cs.GR · cs.CV · cs.LG

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Add to your LaTeX paper

\usepackage{pith}
\pithnumber{LWT5KWEGTTPUELAD4SSXK45EWB}

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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

Experimental results demonstrate that MoZoo achieves high-fidelity fur simulation across diverse animal skeletons and layouts, preserving superior temporal and structural consistency.

C2weakest assumption

The synthetic-to-real pipeline used to create MoZoo-Data produces training examples whose distribution is close enough to real animal videos that the model generalizes without large domain gaps or artifacts.

C3one line summary

MoZoo generates high-fidelity animal videos with fur and muscle dynamics from coarse meshes by extending video diffusion with role-aware RoPE and asymmetric decoupled attention, trained on a new synthetic-to-real dataset.

References

56 extracted · 56 resolved · 17 Pith anchors

[1] SAM 3: Segment Anything with Concepts 2025 · arXiv:2511.16719

[2] Chang, D., Hou, J., Bozic, A., Neuberger, A., Juefei-Xu, F., Maury, O., Lin, G.W.C., Stuyck, T., Roble, D., Soleymani, M., Grabli, S.: Hairweaver: Few-shot photorealistic hair motion synthesis with si 2026

[3] Chen, H., Zhang, Y., Cun, X., Xia, M., Wang, X., Weng, C., Shan, Y.: Videocrafter2: Overcoming data limitations for high-quality video diffusion models (2024) 2024

[4] ACM SIGGRAPH 2015 Talks , articleno = 2015 · doi:10.1145/2775280.2792559

[5] Diffusion Models Beat GANs on Image Synthesis 2021 · arXiv:2105.05233

Receipt and verification

First computed	2026-05-17T23:39:19.534537Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5da7d558869cdf422c03e4a57573a4b07d42b9dca9686f8ec172dc3d13b4a756

Aliases

arxiv: 2605.13857 · arxiv_version: 2605.13857v1 · doi: 10.48550/arxiv.2605.13857 · pith_short_12: LWT5KWEGTTPU · pith_short_16: LWT5KWEGTTPUELAD · pith_short_8: LWT5KWEG

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/LWT5KWEGTTPUELAD4SSXK45EWB \
  | 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: 5da7d558869cdf422c03e4a57573a4b07d42b9dca9686f8ec172dc3d13b4a756

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "61b3da92e2803c5707d82aecbb4c408ff4c5e9a8e356cb8c4f8ae44c90aab115",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.GR",
    "submitted_at": "2026-04-08T15:42:16Z",
    "title_canon_sha256": "0071f59056260dd9dfa6f732359bc6d8097682bce19ac7851573542594c19b9b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13857",
    "kind": "arxiv",
    "version": 1
  }
}