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pith:2026:TIH647K3IUPW5WXOAVHHOOJ2WV
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ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

Bing Zhou, Chuan Guo, Hojun Jang, Inwoo Hwang, Jian Wang, Young Min Kim

ScaleMoGen generates human motions by autoregressively predicting discrete tokens from coarse to fine skeletal-temporal scales.

arxiv:2605.11704 v2 · 2026-05-12 · cs.CV

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Claims

C1strongest claim

ScaleMoGen achieves state-of-the-art performance, establishing an FID of 0.030 (vs. 0.045 for MoMask) on HumanML3D and a CLIP Score of 0.693 (vs. 0.685 for MoMask++) on the SnapMoGen dataset, while enabling training-free text-guided motion editing.

C2weakest assumption

That quantizing 3D motions into compositional discrete tokens across multiple skeletal-temporal scales strictly preserves the skeletal hierarchy and that bitwise quantization stabilizes optimization for high-detail motions.

C3one line summary

ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.

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1 paper in Pith

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First computed 2026-07-22T01:23:57.266373Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

9a0fee7d5b451f6edaee054e77393ab56c054664725112628f58e7e309adf5bb

Aliases

arxiv: 2605.11704 · arxiv_version: 2605.11704v2 · doi: 10.48550/arxiv.2605.11704 · pith_short_12: TIH647K3IUPW · pith_short_16: TIH647K3IUPW5WXO · pith_short_8: TIH647K3
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/TIH647K3IUPW5WXOAVHHOOJ2WV \
  | 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())"
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Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-12T07:58:58Z",
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