Pith Number

pith:TMF5MVBU

pith:2026:TMF5MVBUVAKCE6OKN5BIMOYLUN

not attested not anchored not stored refs resolved

Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation

Junhyuk Jeon, Junyong Noh, Seokhyeon Hong

A hypernetwork maps style embeddings from reference motions to LoRA parameters that modulate a pretrained text-to-motion diffusion model at every denoising step.

arxiv:2605.13333 v1 · 2026-05-13 · cs.CV · cs.AI · cs.GR · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{TMF5MVBUVAKCE6OKN5BIMOYLUN}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

We propose a lightweight style conditioning framework that dynamically modulates a pretrained diffusion model through hypernetwork-generated LoRA parameters.

C2weakest assumption

That a hypernetwork can map global style embeddings to effective low-rank updates applied at every denoising step while preserving text alignment and generalizing to unseen styles without post-hoc tuning or loss of motion quality.

C3one line summary

A hypernetwork maps style motion embeddings to LoRA updates that stylize text-driven motion diffusion models with improved generalization to unseen styles via contrastive structuring of the style space.

References

46 extracted · 46 resolved · 5 Pith anchors

[1] European Conference on Computer Vision , pages= 2024

[2] Computer Graphics Forum , pages= 2025

[3] Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers , pages=

[4] Proceedings of the IEEE/CVF international conference on computer vision , pages=

[5] Lora: Low-rank adaptation of large language models. , author=. ICLR , volume=

Receipt and verification

First computed	2026-05-18T02:44:48.519369Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9b0bd65434a8142279ca6f42863b0ba34311d5b21f0b69da683212c85810fabb

Aliases

arxiv: 2605.13333 · arxiv_version: 2605.13333v1 · doi: 10.48550/arxiv.2605.13333 · pith_short_12: TMF5MVBUVAKC · pith_short_16: TMF5MVBUVAKCE6OK · pith_short_8: TMF5MVBU

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/TMF5MVBUVAKCE6OKN5BIMOYLUN \
  | 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: 9b0bd65434a8142279ca6f42863b0ba34311d5b21f0b69da683212c85810fabb

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a033bd221acdac9178159db2ce5e389b69f684ea31872793e55e862d113bdf84",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.GR",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T10:51:54Z",
    "title_canon_sha256": "fb688950ac095ea54b35ff00200fb8571262dd51bee140bbaf33bfeedfe6fa7c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13333",
    "kind": "arxiv",
    "version": 1
  }
}