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arxiv: 2606.19073 · v1 · pith:THACJMIGnew · submitted 2026-06-17 · 💻 cs.CV

Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework

Jiayi Gao , Qingchao Chen , Yuxin Peng , Yang Liu This is my paper

Pith reviewed 2026-06-26 21:29 UTC · model grok-4.3

classification 💻 cs.CV
keywords human-object interactionimage editingimage-to-videoself-correcting frameworkbenchmarkcognitive levelsinteraction evaluationagentic editing
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The pith

I2V models with an agentic self-correcting framework achieve competitive performance on dynamic human-object interaction image editing.

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

The paper shows that standard image editing struggles with complex human-object interactions because existing benchmarks mix them with static changes and use metrics that cannot check both interaction validity and pair preservation at once. It introduces the HOI-Edit benchmark with three cognitive levels and an automated HOI-Eval metric that uses vision-language model question answering on grounded pairs. Benchmarking reveals that image-to-video models are naturally suited for the task because their temporal generation supplies both better interaction modeling and a replay of how failures unfold. The authors then present SCPE, which iteratively refines prompts to steer the I2V output until the target interaction appears correctly, after which frames are extracted as the edited image. On the new benchmark SCPE reaches performance levels comparable to leading editing models on the interaction aspect.

Core claim

By treating dynamic relationship remodeling as a temporal generation problem, I2V models can be steered via an agentic self-correcting loop of prompt refinement to produce videos whose extracted frames accurately realize target human-object interactions, delivering results competitive with state-of-the-art image editing models on the HOI-Edit benchmark.

What carries the argument

SCPE (Self-Correcting Process Editing), an agentic loop that iteratively refines prompts to constrain I2V generation so that extracted frames realize the desired HOI.

If this is right

  • I2V models supply a unique diagnostic capability by allowing inspection of the generation sequence that led to an interaction error.
  • Extracted frames from the final corrected I2V output serve as the edited still image.
  • The HOI-Eval metric, by querying a VLM after it sees grounded human-object pairs, can assess both interaction correctness and pair preservation simultaneously.
  • Progressive cognitive levels in HOI-Edit let researchers measure editing difficulty from simple attribute changes to complex dynamic interactions.

Where Pith is reading between the lines

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

  • The same self-correcting loop could be tested on other temporal editing tasks such as motion transfer or scene dynamics where static models currently fail.
  • If the diagnostic replay property holds, it may allow automated error classification that feeds back into prompt refinement without human intervention.
  • The benchmark construction suggests that future interaction benchmarks should separate static attribute control from relational dynamics rather than conflating them.

Load-bearing premise

Image-to-video models are inherently better suited than static image models for editing dynamic human-object interactions because their temporal generation both improves results and supplies diagnostic replays of errors.

What would settle it

A direct comparison in which SCPE applied to I2V models scores lower than leading static editing models on the HOI-Eval interaction metric across the three cognitive levels of HOI-Edit.

Figures

Figures reproduced from arXiv: 2606.19073 by Jiayi Gao, Qingchao Chen, Yang Liu, Yuxin Peng.

Figure 1
Figure 1. Figure 1: Overview. We present (A) HOI-Edit, the first benchmark forHOI editing across 3 cognitive levels; (B) HOI-Eval, a novel metric for verifying HOI correctness; and (C) SCPE, an agentic framework optimizing I2V models’ HOI editing ability. ment (e.g., “into the vase"), ensuring dynamic interactions align precisely with spatial descriptions. L3: Causal and Physical Reasoning requires simulating event causal cha… view at source ↗
Figure 2
Figure 2. Figure 2: Illustrative examples for hierarchical cognitive level in HOIEdit. nally, we designed a comprehensive grounding-based Q&A suite tailored to distinct evaluation needs: First, to assess core pair-wise subject-object interactions, we constructed two pair-wise identity questions to strictly verify subject and object retention, respectively(e.g., Q1-L1, Q2-L1) and one interaction status question to confirm the … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of benchmark construction and evaluation pipeline [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: HOI-Edit data distribution [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on HOI-Edit. where ‘Qwen Plus’ stands for Qwen-Image-Edit PLus I2V Model’s Potential. Interestingly, despite the perfor￾mance gap, we observe a unique advantage in I2V models over static baselines. Unlike the opaque, irreversible arti￾facts in static editing (which only reveal what went wrong), I2V outputs provide a “replay of the execution process.” which exposes why the failure occ… view at source ↗
Figure 8
Figure 8. Figure 8: Pipeline of SCPE. specific rightmost chisel as a running example: The Gener￾ator first queries the Playbook—a dynamic, initially empty knowledge base evolving via iterations—and leverages this knowledge to integrate the initial instruction with the in￾put image, producing an enhanced instruction that guides the I2V model to precisely execute the target HOI editing; the generated video (top frames) shows su… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization for prompt enhance process. 5. Self-Correcting Process Editing To answer this question affirmatively, we propose SCPE (Self-Correcting Process Editing). Unified by the back￾bone of Gemini 2.5 Pro (Comanici et al., 2025), SCPE operates as a closed-loop system where specialized agents leverage the model’s multimodal capabilities (for Genera￾tor/Analyzer) or textual reasoning (for Reflector/Cura… view at source ↗
Figure 9
Figure 9. Figure 9: Subject-object similarity comparison: HOI-Eval vs global metrics (instruction: place jacket on car hood) [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Iteration Analysis. 6.2. Ablation Study Metric Reliability and User Study. To verify HOI-Eval metrics’ reliability, we analyzed correlations between its automated metrics (interaction editing, identity preserva￾tion) and human evaluations ( [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Visualization for Playbook How Playbook enhance the instruction. Fig11 shows rep￾resentative Playbook entries targeting the dominant failure modes across 3 cognitive levels. The mechanism operates as a "diagnosis-and-cure" system: for each level, the agent identifies Pitfalls—such as static optimization bias (L1), spa￾tial ambiguity (L2), or causal shortcuts (L3)—and retrieves corresponding Strategies to … view at source ↗
Figure 12
Figure 12. Figure 12: Fine-grained interaction score across seven sub-dimensions of HOI-Edit. For sub-dimensions in L1, we report I, for sub-dimensions in L2/3,we report I+Q&A D.2. More Visualization [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative comparison for L1 & L2. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Additional visualizations on complex HOI scenarios involving phycial and causal chains. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
read the original abstract

Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by existing benchmarks that conflate HOI with static attributes, relying on global metrics incapable of simultaneously assessing dynamic interaction validity and entangled human-object pair preservation. Thus, we first introduce HOI-Edit, a comprehensive benchmark with three progressive cognitive levels, which features an automated metric HOI-Eval that reliably evaluates instance-level interaction by letting VLM Q&A after thinking with images containing grounded Human-Object pairs. Considering the task's essence of remodeling dynamic relationships, we benchmark Image-to-Video (I2V) models, finding them inherently suited for dynamic editing due to their temporal generation capabilities. Crucially, beyond superior performance, this capability provides a "replay of the failure process," offering unique diagnosability into why errors occur. We thus propose SCPE (Self-Correcting Process Editing), a novel, agentic self-correcting framework that constrains the generation of I2V models through iteratively refined prompts, enabling the generated videos to more accurately present the target HOI. Extracted frames from these videos are the final editing results. On HOI-Edit, SCPE achieves performance competitive with state-of-the-art (SOTA) editing models like Nano Banana on interaction. Code is available at https://github.com/oceanflowlab/HOI-Edit.

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 / 1 minor

Summary. The paper introduces the HOI-Edit benchmark for Human-Object Interaction (HOI) image editing, structured around three progressive cognitive levels, along with the automated HOI-Eval metric that uses VLM-based grounded Q&A to assess instance-level interactions. It benchmarks Image-to-Video (I2V) models as inherently suited for dynamic HOI editing due to temporal capabilities that also enable failure diagnosis, and proposes the SCPE agentic framework that applies iterative prompt refinement to I2V models, extracting final edited frames from the generated videos. The central empirical claim is that SCPE achieves performance competitive with SOTA editing models such as Nano Banana specifically on the interaction dimension of the HOI-Edit benchmark.

Significance. If the HOI-Eval metric proves reliable through human correlation and the reported competitive results hold under rigorous controls, the work would provide a much-needed cognitive benchmark for HOI editing and demonstrate a practical way to leverage I2V temporal modeling plus self-correction for dynamic interactions. The public code release would further strengthen reproducibility and enable follow-on research.

major comments (2)
  1. [Abstract] Abstract: The headline claim that SCPE is competitive with SOTA models rests entirely on HOI-Eval scores, yet the manuscript provides no reported correlation between HOI-Eval and human judgments, no inter-annotator agreement figures, and no comparison against existing metrics. This validation gap is load-bearing because an unproven automated evaluator cannot establish that the generated interactions are actually valid or that SCPE outperforms baselines on the intended cognitive dimensions.
  2. [Abstract] Abstract and evaluation description: No experimental details, error bars, statistical tests, or full baseline comparisons are visible to support the competitive performance numbers, leaving the central claim without visible supporting data or derivation.
minor comments (1)
  1. [Abstract] Abstract: The model name 'Nano Banana' appears without citation or clarification; if it is a specific published method, a reference should be added.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. The two major comments highlight important gaps in validation and experimental reporting that we will address in the revision. We respond point-by-point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that SCPE is competitive with SOTA models rests entirely on HOI-Eval scores, yet the manuscript provides no reported correlation between HOI-Eval and human judgments, no inter-annotator agreement figures, and no comparison against existing metrics. This validation gap is load-bearing because an unproven automated evaluator cannot establish that the generated interactions are actually valid or that SCPE outperforms baselines on the intended cognitive dimensions.

    Authors: We agree that explicit validation of HOI-Eval against human judgments is essential to support its reliability for assessing instance-level HOI. The manuscript describes the VLM-based grounded Q&A procedure but does not include the requested correlation analysis, inter-annotator agreement, or comparisons to prior metrics. We will add a new subsection (likely in Section 4) reporting human correlation studies, inter-annotator agreement statistics (e.g., Cohen’s kappa or Krippendorff’s alpha), and direct comparisons against existing metrics such as CLIPScore and HOI-specific baselines. These additions will be used to substantiate the claim that HOI-Eval reliably captures the intended cognitive dimensions. revision: yes

  2. Referee: [Abstract] Abstract and evaluation description: No experimental details, error bars, statistical tests, or full baseline comparisons are visible to support the competitive performance numbers, leaving the central claim without visible supporting data or derivation.

    Authors: We acknowledge that the abstract and main evaluation sections currently lack the requested experimental rigor. In the revised manuscript we will expand the evaluation section to include: (i) error bars (standard deviation across multiple runs or seeds), (ii) statistical significance tests (e.g., paired t-tests or Wilcoxon signed-rank tests with p-values), and (iii) complete baseline tables covering all three cognitive levels of HOI-Edit with all relevant SOTA editing models. The abstract will be updated to reference these detailed results. These changes will make the competitive performance claim fully traceable and reproducible. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on external SOTA comparisons and new benchmark

full rationale

The paper introduces HOI-Edit benchmark and HOI-Eval metric, benchmarks I2V models empirically, and proposes SCPE framework whose performance is reported as competitive with external models (e.g., Nano Banana) on the new benchmark. No equations, fitted parameters renamed as predictions, self-citations, or definitional reductions are present in the provided text. The central claim derives from reported scores against independent baselines rather than any input by construction, satisfying the self-contained criterion against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim depends on the domain assumption that I2V temporal capabilities directly enable accurate HOI remodeling and that VLM-based Q&A reliably measures interaction validity; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption I2V models are inherently suited for dynamic editing due to their temporal generation capabilities
    Explicitly stated as a crucial finding that underpins both benchmarking and the SCPE framework.

pith-pipeline@v0.9.1-grok · 5797 in / 1172 out tokens · 32278 ms · 2026-06-26T21:29:18.593054+00:00 · methodology

discussion (0)

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