arxiv: 2604.14069 · v1 · submitted 2026-04-15 · 💻 cs.CV

Recognition: unknown

Towards Unconstrained Human-Object Interaction

Alessandro Conti, Cigdem Beyan, Elisa Ricci, Francesco Tonini, Lorenzo Vaquero

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:13 UTC · model grok-4.3

classification 💻 cs.CV

keywords human-object interaction detectionunconstrained tasksmultimodal large language modelsopen vocabularylanguage-to-graph conversionin-the-wild recognitioncomputer vision

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The pith

Multimodal language models can detect human-object interactions in the wild without any predefined list of actions.

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

The paper defines an unconstrained human-object interaction task that removes the need for a fixed vocabulary of interactions during both training and inference. It applies multimodal large language models to generate free-form text descriptions of scenes and converts those descriptions into structured graphs of interactions. Traditional detectors are limited because they cannot recognize actions outside their training set, while the new approach handles open-ended, real-world cases. A pipeline using test-time inference enables this flexibility without retraining. If the approach holds, it opens detection to unpredictable scenes where any interaction might occur.

Core claim

The paper establishes the Unconstrained HOI task, which eliminates any requirement for a predefined interaction vocabulary at training and inference, and demonstrates that multimodal large language models can address it through test-time inference followed by language-to-graph conversion to extract structured interaction representations from free-form text outputs.

What carries the argument

The Unconstrained HOI task, which drops the predefined interaction vocabulary at both training and inference, paired with language-to-graph conversion that structures free-form text outputs into interaction representations.

If this is right

Current detectors limited to fixed vocabularies cannot generalize to novel interactions encountered in unconstrained scenes.
Multimodal models enable recognition of arbitrary interactions by generating natural language descriptions instead of selecting from a closed list.
Test-time inference allows adaptation to new environments without additional model training.
Structured interaction data remains extractable from open-ended text outputs through the conversion step.

Where Pith is reading between the lines

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

This task definition could apply to other open-vocabulary vision problems where interactions or actions cannot be fully enumerated in advance.
Combining the pipeline with video streams might support real-time monitoring in environments with unpredictable human-object contacts.
Lower annotation requirements for new domains could arise if models generate descriptions without needing exhaustive predefined lists.
Chaining the output graphs with downstream reasoning systems might enable higher-level scene interpretation beyond pairwise interactions.

Load-bearing premise

The conversion from the language model's free-form text descriptions to structured interaction graphs occurs without significant parsing errors or loss of scene context.

What would settle it

A set of images showing rare or novel human-object interactions where the extracted graphs from model text are compared against human annotations for accuracy and completeness.

Figures

Figures reproduced from arXiv: 2604.14069 by Alessandro Conti, Cigdem Beyan, Elisa Ricci, Francesco Tonini, Lorenzo Vaquero.

**Figure 2.** Figure 2: AnyHOI first detects human-object pairs using an object detector. Then, it crops the region encompassing both the human and its paired object and feeds it into an MLLM, along with a prompt to generate free-form scene descriptions. A post-generation refinement step then analyzes these descriptions to produce a relationship graph. Finally, the graph is refined by filtering out the triplets that are irrelevan… view at source ↗

**Figure 3.** Figure 3: The test-time compute strategy generates multiple responses using its baseline MLLM, producing a diverse set of interaction proposals, which are aggregated and sampled based on their frequency, yielding the final predictions. a consequence, this narrow focus overlooks subtler interactions that may be less prominent in the image. Furthermore, this issue is exacerbated by the size of fMLLM, with smaller mod… view at source ↗

**Figure 4.** Figure 4: Qualitative results of AnyHOI and CLIP [52] on the HICO-DET [5] dataset [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Evaluation of DHD [64] on the VG-HOI [64] dataset with and [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

**Figure 6.** Figure 6: Results of LLaVA + AnyHOI + TT varying the number of [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 7.** Figure 7: mAP of MLLMs on grouped verbs by topic. Pushing Pulling Catching Throwing Sitting on Standing on 0 10 20 30 40 50 60 Avg. OmAP (%) Models performance on opposite verbs CogVLM2 19B IDEFICS2 8B InstructBLIP 13B InternVL 8B LLaVA OV 7B Phi3V 4B Qwen2-VL 7B [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: mAP of MLLMs on opposite verbs. Hugging Kissing Petting Pointing Waving 0 10 20 30 40 50 60 Avg. OmAP (%) Models performance on social interaction verbs CogVLM2 19B IDEFICS2 8B InstructBLIP 13B InternVL 8B LLaVA OV 7B Phi3V 4B Qwen2-VL 7B [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: mAP of MLLMs of social interaction verbs. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗

**Figure 10.** Figure 10: mAP of MLLMs on object manipulation verbs. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗

**Figure 11.** Figure 11: mAP of MLLMs performance on verbs with propositions [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗

**Figure 12.** Figure 12: Qualitative results of LLaVA OV 0.5B [27] + AnyHOI + TT and CLIP [52] on the HICO-DET [5] annotated-box setting. Our proposed AnyHOI [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗

**Figure 13.** Figure 13: Qualitative results of LLaVA OV 0.5B [27] + AnyHOI + TT, and the intermediate outputs, on the HICO-DET [5] annotated-box setting. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗

read the original abstract

Human-Object Interaction (HOI) detection is a longstanding computer vision problem concerned with predicting the interaction between humans and objects. Current HOI models rely on a vocabulary of interactions at training and inference time, limiting their applicability to static environments. With the advent of Multimodal Large Language Models (MLLMs), it has become feasible to explore more flexible paradigms for interaction recognition. In this work, we revisit HOI detection through the lens of MLLMs and apply them to in-the-wild HOI detection. We define the Unconstrained HOI (U-HOI) task, a novel HOI domain that removes the requirement for a predefined list of interactions at both training and inference. We evaluate a range of MLLMs on this setting and introduce a pipeline that includes test-time inference and language-to-graph conversion to extract structured interactions from free-form text. Our findings highlight the limitations of current HOI detectors and the value of MLLMs for U-HOI. Code will be available at https://github.com/francescotonini/anyhoi

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

The paper defines an unconstrained HOI task and sketches an MLLM pipeline, but the language-to-graph conversion remains unvalidated and no numbers are shown to back the superiority claims.

read the letter

The core move here is defining U-HOI so that neither training nor inference is locked to a fixed interaction vocabulary, then routing MLLM free-form descriptions through a language-to-graph step to produce evaluable outputs. That task framing is new relative to the closed-vocab HOI literature they cite, and the pipeline idea is a straightforward way to let models describe interactions they were never explicitly trained on. The abstract correctly flags that standard detectors break when the action set is open, and the plan to release code is useful for anyone who wants to test the approach themselves. Those are the concrete contributions. The soft spot is exactly the one the stress-test flags. The conversion from raw MLLM text to structured interaction graphs is load-bearing for any claim that MLLMs outperform constrained detectors, yet the abstract supplies no validation of that step against human-annotated graphs on the same images, no error rates on parsing, and no quantitative results at all. Without those checks, reported findings rest on an assumption that the parser neither drops context nor hallucinates relations in unconstrained scenes. If the full paper contains such checks, they are not visible in the summary provided. The work is aimed at groups already working on open-vocabulary or zero-shot interaction detection and on structured extraction from LLM outputs. It is coherent on its own terms and engages the prior literature, so it deserves a serious referee even though the current evidence is thin. I would send it out for review with the expectation that the authors strengthen the parser validation and add concrete metrics before acceptance.

Referee Report

2 major / 0 minor

Summary. The paper defines the Unconstrained HOI (U-HOI) task, which removes the requirement for a predefined list of interactions at both training and inference. It evaluates a range of MLLMs on in-the-wild settings and introduces a pipeline with test-time inference and language-to-graph conversion to extract structured interactions from free-form text, claiming this highlights limitations of current HOI detectors and the value of MLLMs for U-HOI.

Significance. If the pipeline's conversion step holds up under validation, the work could meaningfully advance flexible, open-vocabulary HOI detection for unconstrained real-world scenes, leveraging MLLMs to overcome fixed-vocabulary constraints in traditional detectors.

major comments (2)

The language-to-graph conversion is load-bearing for all claims about MLLM performance and superiority, yet the manuscript provides no validation of this step (e.g., agreement with human-annotated graphs on the same images) or analysis of parsing errors, hallucinations, or context loss in unconstrained scenes.
Although the abstract states that evaluations of MLLMs were performed, the text supplies no quantitative metrics, baselines, dataset details, or error analysis, preventing verification that the data support the claims of limitations and value.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our introduction of the U-HOI task and the MLLM-based pipeline. We address the major comments point by point below.

read point-by-point responses

Referee: The language-to-graph conversion is load-bearing for all claims about MLLM performance and superiority, yet the manuscript provides no validation of this step (e.g., agreement with human-annotated graphs on the same images) or analysis of parsing errors, hallucinations, or context loss in unconstrained scenes.

Authors: We agree that the language-to-graph conversion step requires explicit validation to support the performance claims. The manuscript currently describes the conversion rules and pipeline but does not include quantitative agreement metrics or error analysis. In the revision we will add a dedicated validation subsection that reports inter-annotator agreement on a held-out set of images, together with a breakdown of parsing errors, hallucinations, and context-loss cases observed in unconstrained scenes. revision: yes
Referee: Although the abstract states that evaluations of MLLMs were performed, the text supplies no quantitative metrics, baselines, dataset details, or error analysis, preventing verification that the data support the claims of limitations and value.

Authors: We acknowledge that the presentation of the experimental results is insufficiently detailed. While the manuscript contains some evaluation descriptions, it lacks the full set of quantitative metrics, baseline comparisons, dataset specifications, and error analysis needed for verification. The revised version will expand Sections 4 and 5 to include explicit tables of metrics, baseline results from existing HOI detectors, precise dataset construction details, and a systematic error analysis that directly supports the stated claims about limitations of current detectors and the value of MLLMs for U-HOI. revision: yes

Circularity Check

0 steps flagged

No circularity detected in task definition or pipeline

full rationale

The paper introduces the U-HOI task and an empirical pipeline using MLLMs plus language-to-graph conversion, with no mathematical derivations, equations, fitted parameters, or self-citations that reduce any claimed result to its own inputs by construction. Evaluation relies on external model outputs and conversion heuristics rather than self-referential definitions or predictions forced by prior fits. The conversion step is an unvalidated methodological choice but does not constitute circularity under the specified patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unverified assumption that current MLLMs produce sufficiently accurate and complete free-form descriptions of interactions and that a deterministic language-to-graph parser can turn those descriptions into reliable structured output.

axioms (1)

domain assumption Multimodal large language models can produce reliable free-form descriptions of human-object interactions from visual input.
The entire pipeline depends on this capability; the abstract provides no evidence or error analysis for it.

invented entities (1)

Unconstrained HOI (U-HOI) task no independent evidence
purpose: A new task formulation that removes any requirement for a predefined interaction vocabulary.
Defined within the paper; no external benchmark or independent validation is mentioned.

pith-pipeline@v0.9.0 · 5487 in / 1348 out tokens · 80366 ms · 2026-05-10T14:13:40.811170+00:00 · methodology

discussion (0)

Reference graph

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A. Zunino, J. Cavazza, R. V olpi, P. Morerio, A. Cavallo, C. Becchio, and V . Murino. Predicting intentions from motion: The subject- adversarial adaptation approach.International Journal of Computer Vision, 128:220–239, 2020. Towards Unconstrained Human-Object Interaction Supplementary Material In this document we first analyze the open-vocabulary HOI pa...

work page arXiv 2020