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arxiv: 2605.30969 · v1 · pith:Q25LBNBHnew · submitted 2026-05-29 · 💻 cs.CV

Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning

Zhenwu Shi , Jingyu Gong , Peiwei Wang , Xingzan Wang , Tianwen Qian , Wenxi Li , Yuan Fang , Jiao Xie
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Lizhuang Ma Shaohui Lin
This is my paper

Pith reviewed 2026-06-28 22:59 UTC · model grok-4.3

classification 💻 cs.CV
keywords motion editingtext-based editingpositive-negative learningdiffusion modelshuman motionsemantic alignmentinvariance preservationOmniME
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The pith

A positive-negative supervision framework balances precise text-driven motion edits with preservation of unchanged parts.

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

The paper sets out to show that text-based human motion editing can avoid distortion and poor alignment by using a single framework that combines retrospective feature supervision across transformer layers, a motion preservation mechanism tuned to source-target similarity, and triplet-based semantic alignment. Current diffusion methods rely on heuristics that either change too much or too little. If the claim holds, editing tools would produce motion sequences that follow language instructions exactly where intended while leaving the rest intact.

Core claim

The Omni-Supervised Positive-Negative Learning framework (OmniME) integrates retrospective feature supervision for coarse-to-fine consistency, a motion preservation mechanism focused on subtle variations, and triplet-based semantic alignment to strengthen text-motion correspondence, together forming a unified supervision paradigm that balances change and invariance.

What carries the argument

The three-component Omni-Supervised Positive-Negative Learning framework that enforces layer-wise consistency, similarity-based preservation, and triplet semantic alignment.

If this is right

  • State-of-the-art editing alignment on the MotionFix and STANCE Adjustment datasets.
  • Reduced motion distortion while following text instructions.
  • Stronger correspondence between language commands and the regions that actually change.
  • A reusable supervision pattern that treats edited and unedited regions symmetrically.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same positive-negative structure might transfer to other conditional generation tasks such as video or 3D shape editing.
  • The retrospective supervision idea could be tested on non-diffusion backbones to check if the balance holds outside the current model family.
  • Extending the triplet alignment to longer or multi-sentence instructions would test whether the invariance property scales.

Load-bearing premise

That the three supervision components can be combined into one framework without introducing new distortions or leaving alignment suboptimal.

What would settle it

Quantitative results on the MotionFix dataset where OmniME scores no higher than prior diffusion baselines on editing alignment metrics.

Figures

Figures reproduced from arXiv: 2605.30969 by Jiao Xie, Jingyu Gong, Lizhuang Ma, Peiwei Wang, Shaohui Lin, Tianwen Qian, Wenxi Li, Xingzan Wang, Yuan Fang, Zhenwu Shi.

Figure 1
Figure 1. Figure 1: OmniME is a positive–negative learning framework for text-driven human motion editing. Given a source motion and a natural-language instruction, OmniME edits the source motion to produce the desired target motion while balancing change and in￾variance. 16, 22, 62], human–robot interaction [3, 21, 62], and au￾tonomous behavior simulation [44]. Most existing methods focus on generating motions from control s… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of OmniME: Unified Framework for Human Motion Editing. The source motion and text are first fed into a Fusion Transformer to integrate information and then passed through the Diffusion Transformer (DiT) for denoising and prediction. In Section 3.3, we compute source-target similarity scores and supervise motions with subtle changes. In Section 3.4, multiple intermediate outputs from the transforme… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison between our method and SimMotionEdit[34] on the MotionFix [6] dataset. Our results surpass SimMotionEdit in terms of semantic consistency, motion smoothness, and source motion preservation. ③ “Wave the other arm” ④ “Both arms are slightly raised” Source Motion Ground Truth SimMotionEdit Ours Source Motion Ground Truth SimMotionEdit Ours [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison between our method and SimMotionEdit[34] on the STANCE Adjustment [28] dataset. Our results surpass SimMotionEdit in terms of semantic consistency, motion smoothness, and source motion preservation. ture the nuance of “slightly”, whereas our method accu￾rately adjusts the arms to shoulder level, aligning better with the intended semantics. Overall, our approach outperforms SimMotionE… view at source ↗
Figure 5
Figure 5. Figure 5: Perceptual Study Comparison between OmniME and SimMotionEdit[34]. Average user ratings (1–10 scale) on four evaluation criteria: Semantic Alignment, Motion Preserva￾tion, Transition Smoothness, and Overall Naturalness. Left: Mo￾tionFix dataset [6]. Right: STANCE Adjustment dataset [28]. Our method consistently outperforms SimMotionEdit in all evaluation dimensions across the datasets. dataset [28]. For eac… view at source ↗
read the original abstract

Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of the original motion. Existing diffusion-based approaches often rely on heuristic similarity cues or coarse global conditioning, leading to motion distortion and suboptimal semantic alignment. The key challenge lies in balancing change (i.e. precisely editing target regions) and invariance (i.e. preserving unedited parts). To handle such challenge, we propose an Omni-Supervised Positive-Negative Learning framework, named OmniME. Our method integrates three complementary components: (1) retrospective feature supervision that enforces coarse-to-fine consistency across transformer layers,(2) motion preservation mechanism that focuses on subtle variations according to the source-target similarity, and (3) triplet-based semantic alignment that strengthens text-motion correspondence. Together, these components form a unified supervision paradigm that balances change and invariance. Extensive experiments on the MotionFix and STANCE Adjustment datasets demonstrate that OmniME achieves state-of-the-art performance in editing alignment, validating the effectiveness of our unified learning framework. Our source codes and models have been released at: https://github.com/rocket-ycyer/OmniME.git

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.

Referee Report

2 major / 0 minor

Summary. The paper proposes OmniME, an Omni-Supervised Positive-Negative Learning framework for text-based human motion editing. It integrates retrospective feature supervision for coarse-to-fine consistency across transformer layers, a motion preservation mechanism based on source-target similarity, and triplet-based semantic alignment to strengthen text-motion correspondence. These components are claimed to form a unified paradigm balancing change and invariance. The manuscript asserts state-of-the-art performance on the MotionFix and STANCE Adjustment datasets via extensive experiments.

Significance. If the SOTA claims hold with proper validation, the work could advance text-conditioned motion editing by offering a more principled positive-negative supervision strategy that mitigates distortion and improves semantic alignment, with potential benefits for animation and interactive applications.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'OmniME achieves state-of-the-art performance in editing alignment' is unsupported by any quantitative metrics, baseline comparisons, tables, or error analysis, which is load-bearing for validating the unified learning framework.
  2. [Abstract] Abstract: No equations, implementation details, or ablation results are supplied for the three components (retrospective feature supervision, motion preservation mechanism, triplet-based semantic alignment), preventing verification that they balance change and invariance without introducing new distortions or suboptimal alignment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract point by point. The full manuscript contains the supporting experiments, equations, and ablations referenced in the abstract summary.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'OmniME achieves state-of-the-art performance in editing alignment' is unsupported by any quantitative metrics, baseline comparisons, tables, or error analysis, which is load-bearing for validating the unified learning framework.

    Authors: We agree the abstract would be strengthened by including key quantitative highlights. The full manuscript (Section 4) reports SOTA results on MotionFix and STANCE with tables comparing against baselines, including alignment metrics and error analysis. We will revise the abstract to add concise performance numbers and baseline references while preserving its length. revision: yes

  2. Referee: [Abstract] Abstract: No equations, implementation details, or ablation results are supplied for the three components (retrospective feature supervision, motion preservation mechanism, triplet-based semantic alignment), preventing verification that they balance change and invariance without introducing new distortions or suboptimal alignment.

    Authors: Abstracts are high-level summaries and do not conventionally include equations or ablations; these appear in the main text (Section 3 for component formulations and Section 4 for ablations demonstrating the change-invariance balance). The manuscript structure follows standard practice and enables verification. No revision to the abstract is needed on this point. revision: no

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The provided abstract and description contain no equations, fitted parameters, predictions, or self-citations. The framework is presented as the integration of three named components whose joint effect is asserted to balance change and invariance; this is a conventional high-level claim structure without any reduction of outputs to inputs by construction. The SOTA performance claim is attributed to experiments on external datasets, which constitutes independent empirical support rather than a self-referential derivation. No load-bearing step reduces to a fit, renaming, or self-citation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; no learning rates, loss weights, or new postulated objects are named.

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