arxiv: 2605.11704 · v1 · submitted 2026-05-12 · 💻 cs.CV

Recognition: no theorem link

ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

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

Authors on Pith no claims yet

Pith reviewed 2026-05-13 06:20 UTC · model grok-4.3

classification 💻 cs.CV

keywords human motion generationtext-to-motionautoregressive predictionmulti-scale tokensskeletal hierarchydiscrete quantizationmotion editing

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The pith

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

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

ScaleMoGen treats text-driven human motion generation as a coarse-to-fine process instead of standard next-token prediction. It first quantizes 3D motions into compositional discrete tokens that span multiple skeletal-temporal scales of rising detail, then trains the model to predict the token map for each next scale in sequence. The tokenizers are built to keep the body's skeletal hierarchy intact at every level, while bitwise quantization enlarges the vocabulary and keeps training stable for detailed motions. If the approach holds, the result is higher-fidelity motions that also support direct text-based edits without any additional training. The method records an FID of 0.030 on HumanML3D and a CLIP score of 0.693 on SnapMoGen, both ahead of prior autoregressive baselines.

Core claim

ScaleMoGen frames motion generation as autoregressive next-scale prediction: 3D motions are quantized into compositional discrete tokens across multiple skeletal-temporal scales of increasing granularity, and the model learns to generate motion by predicting the next-scale token maps. Motion tokenizers and quantizers are explicitly designed so that discrete tokens at every scale strictly preserve the skeletal hierarchy. Bitwise quantization and prediction are used to scale up the tokenizer vocabulary while preserving motion details and stabilizing optimization.

What carries the argument

scale-wise autoregressive prediction of next-scale token maps from multi-scale motion tokenizers that preserve skeletal hierarchy

Load-bearing premise

Quantizing 3D motions into compositional discrete tokens across multiple skeletal-temporal scales preserves the skeletal hierarchy and motion details without loss.

What would settle it

Generate motions on a held-out set of complex actions and measure whether the skeletal joint angles or bone lengths at the finest scale deviate from ground-truth values by more than the reported baseline error.

Figures

Figures reproduced from arXiv: 2605.11704 by Bing Zhou, Chuan Guo, Hojun Jang, Inwoo Hwang, Jian Wang, Young Min Kim.

**Figure 1.** Figure 1: Overview of our skeletal-temporal multi-scale motion quantization pipeline. Given an input motion sequence m, the encoder E maps it to a continuous skeletaltemporal latent grid f. The latent is decomposed into a hierarchy of residual components {q v } V v=0 via binary multi-scale residual quantization, where each scale has its own temporal resolution and skeletal partition. The quantized residuals are th… view at source ↗

**Figure 2.** Figure 2: (a) Text-to-Motion Generation: Given a prompt cs, we autoregressively predict the next-scale token maps {q v s } V v=0 conditioned on all coarser-scale token maps. (b) Text-Driven Motion Editing: With additional target prompt ct with a source-token preservation mask {Mv } V v=0, we predict edited tokens q ′(v) t conditioned on the remaining source motion context and ct. The target token maps {q v t } V v=… view at source ↗

**Figure 3.** Figure 3: Qualitative text-to-motion generation results of ScaleMoGen. Given highly descriptive, long-form text prompts, ScaleMoGen accurately synthesizes complex sequences of actions (top-left), fine-grained body-part articulations (top-right), and timely executed motions with precise spatial constraints (bottom). 4.1 Text-to-Motion Generation Tables 1 and 2 show the results on HumanML3D and SnapMoGen. On HumanML… view at source ↗

**Figure 4.** Figure 4: Qualitative results of text-driven motion editing. Given a source motion and a new target description, ScaleMoGen accurately synthesizes the desired semantic changes, while preserving the identity and unrelated behaviors of the original source motion. baselines: MDM [35] and SALAD [16]. For each method, we first generate a source motion from the source text using its own generation pipeline, then apply the… view at source ↗

**Figure 5.** Figure 5: The full-body skeleton is spatially downsampled by merging adjacent joints’ information into coarser anatomical groups, as indicated by the color-coded regions. The initial full-resolution skeleton accommodates both the 22-joint HumanML3D format and the 24-joint SnapMoGen format. Through successive pooling stages, the model effectively captures skeletal complexity and represents the human body in an atomi… view at source ↗

**Figure 6.** Figure 6: Visualization of the intermediate accumulated token map motion reconstruction. Coarse token map captures global motions, which progressively disentangle into independent, fine-grained joint movements at finer scales, restoring full motion realism [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗

read the original abstract

We present ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Unlike conventional autoregressive approaches that rely on standard next-token prediction, ScaleMoGen frames motion generation as a coarse-to-fine process. We quantize 3D motions into compositional discrete tokens across multiple skeletal-emporal scales of increasing granularity, learning to generate motion by autoregressively predicting next-scale token maps. To maintain structural integrity, our motion tokenizers and quantizers are explicitly designed so that discrete tokens at every scale strictly preserve the skeletal hierarchy. Additionally, we employ bitwise quantization and prediction, which efficiently scale up the tokenizer vocabulary to preserve motion details and stabilize optimization. Extensive experiments demonstrate that 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. Furthermore, we demonstrate that our skeletal-temporal multi-scale representation naturally facilitates training-free, text-guided motion editing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ScaleMoGen's multi-scale token prediction is a clean departure from flat autoregressive motion models and delivers small but consistent metric gains plus usable editing.

read the letter

The main contribution is framing motion generation as next-scale token map prediction rather than standard next-token autoregression. They quantize motions into discrete tokens at multiple skeletal-temporal scales, with quantizers built to keep the hierarchy intact at every level, and they add bitwise quantization to grow the vocabulary without destabilizing training. This produces an FID of 0.030 on HumanML3D versus 0.045 for MoMask and a modest CLIP lift on SnapMoGen, along with training-free text-guided editing that follows naturally from the representation. Those are concrete, usable results for people already working in this area. The hierarchy-preserving design and bitwise trick are the parts that feel genuinely new compared to the cited priors. The soft spots are the usual ones for an abstract-only view: no ablations on scale count or vocabulary size, no error bars, and no direct verification that the hierarchy is actually preserved beyond the tokenizer construction. The claim that bitwise quantization stabilizes high-detail optimization is reasonable but not stress-tested in the numbers we see. Overall the central argument holds up on its own terms without obvious contradictions. This is for readers who follow discrete-token motion work and want a practical alternative to MoMask-style models. The idea is distinct enough and the empirical edge is real, so it deserves a serious referee even if the gains stay incremental.

Referee Report

2 major / 2 minor

Summary. The paper introduces ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Motions are quantized into compositional discrete tokens across multiple skeletal-temporal scales of increasing granularity; the model autoregressively predicts next-scale token maps while using bitwise quantization to enlarge vocabulary and stabilize optimization. Tokenizers are designed to preserve skeletal hierarchy at every scale. Experiments report SOTA results (FID 0.030 vs. MoMask 0.045 on HumanML3D; CLIP Score 0.693 vs. MoMask++ 0.685 on SnapMoGen) and demonstrate training-free text-guided editing.

Significance. If the multi-scale quantization and bitwise scheme function as described, the work supplies a concrete coarse-to-fine autoregressive alternative to standard next-token motion models, with measurable metric gains and a practical editing capability that prior single-scale tokenizers lack. The explicit hierarchy-preserving design and empirical comparisons constitute the primary strengths.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): the reported FID improvement (0.030 vs. 0.045) and CLIP-score gain are presented without error bars, multiple random seeds, or explicit dataset-split details; this weakens the claim that the multi-scale construction is responsible for the gains rather than training-protocol differences.
[§3.2] §3.2 (Motion Tokenizer): the statement that tokens at every scale 'strictly preserve the skeletal hierarchy' is load-bearing for the central claim yet lacks an explicit equation or algorithm showing how the compositional quantization enforces this property (e.g., no definition of the per-scale skeletal constraint or proof of invariance under bitwise operations).

minor comments (2)

[§3.3] §3.3: clarify the exact vocabulary sizes chosen for bitwise quantization and report an ablation on the number of skeletal-temporal scales, as these are the two free parameters listed in the design.
[Figure 3 and §4.3] Figure 3 and §4.3: the qualitative editing examples would benefit from side-by-side comparison with a fine-tuned baseline to illustrate the training-free advantage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and will revise the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): the reported FID improvement (0.030 vs. 0.045) and CLIP-score gain are presented without error bars, multiple random seeds, or explicit dataset-split details; this weakens the claim that the multi-scale construction is responsible for the gains rather than training-protocol differences.

Authors: We agree that reporting error bars, multiple random seeds, and explicit dataset-split details would strengthen the statistical robustness of the results. In the revised version, we will add standard deviations computed over at least three independent runs with different seeds, clarify the exact train/validation/test splits used on HumanML3D and SnapMoGen, and include a brief discussion confirming that all baselines were re-evaluated under identical protocols. While the primary gains are attributable to the multi-scale architecture (as supported by our ablation studies), we acknowledge that these additions will better isolate the contribution of the proposed components from training variations. revision: yes
Referee: [§3.2] §3.2 (Motion Tokenizer): the statement that tokens at every scale 'strictly preserve the skeletal hierarchy' is load-bearing for the central claim yet lacks an explicit equation or algorithm showing how the compositional quantization enforces this property (e.g., no definition of the per-scale skeletal constraint or proof of invariance under bitwise operations).

Authors: We concur that an explicit mathematical formulation is needed to substantiate this key property. The preservation arises because each scale's tokenizer operates on a hierarchical skeletal graph where parent joints are quantized before children, and bitwise quantization is applied independently per scale without cross-scale mixing. In the revision, we will insert a formal definition in §3.2 (including the per-scale constraint equation and a short invariance argument under bitwise operations) together with a pseudocode outline of the quantization algorithm to make the enforcement mechanism fully transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper introduces ScaleMoGen as a new autoregressive framework that quantizes motions into multi-scale discrete tokens and predicts next-scale maps, with performance validated through direct empirical comparisons (FID, CLIP scores) against prior published methods on HumanML3D and SnapMoGen. No equations or derivations are presented that reduce predictions or results to fitted parameters defined by the same inputs, self-citations that bear the central claim, or ansatzes smuggled via prior author work. The quantization design is described as explicitly constructed to preserve hierarchy, but this is an architectural choice evaluated externally rather than a tautological self-definition. The approach is self-contained against benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The approach depends on the design choice that multi-scale quantization preserves skeletal structure and on hyperparameters for the number of scales and vocabulary size.

free parameters (2)

number of skeletal-temporal scales
Chosen to enable coarse-to-fine generation while preserving hierarchy.
bitwise quantization vocabulary size
Scaled to preserve motion details without destabilizing training.

axioms (1)

domain assumption Discrete tokens at every scale strictly preserve the skeletal hierarchy
Stated as an explicit design requirement for the motion tokenizers and quantizers.

invented entities (1)

compositional discrete tokens across multiple skeletal-temporal scales no independent evidence
purpose: Enable autoregressive next-scale prediction while maintaining structural integrity
New representation introduced for the coarse-to-fine motion generation process

pith-pipeline@v0.9.0 · 5504 in / 1248 out tokens · 52953 ms · 2026-05-13T06:20:11.668434+00:00 · methodology

discussion (0)

Reference graph

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