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arxiv: 2605.21796 · v1 · pith:FQJ2THGLnew · submitted 2026-05-20 · 💻 cs.CV · cs.CL

MM-Conv: A Multimodal Dataset and Benchmark for Context-Aware Grounding in 3D Dialogue

Anna Deichler , Jim O'Regan , Fethiye Irmak Dogan , Lubos Marcinek , Anna Klezovich , Iolanda Leite , Jonas Beskow This is my paper

Pith reviewed 2026-05-22 08:30 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords multimodal dataset3D groundingcontext-aware dialoguereferring expressionsVR interactionsvisual localizationconversational ambiguity
3
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The pith

Contextual rewriting before visual grounding improves 3D dialogue performance by 11-22 points

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

Current vision-language models perform well on static images but fail when references in conversation are ambiguous and depend on prior turns. This paper creates a benchmark from six hours of VR data that includes speech, gaze, motion and 3D scene information to measure such grounding in dynamic environments. It then shows that first rewriting the referring expressions to remove ambiguity using context, then locating the objects visually, raises accuracy substantially compared with models that attempt both steps together.

Core claim

A two-stage grounding pipeline that first resolves conversational ambiguity through contextual rewriting and then performs visual localization outperforms end-to-end approaches. On the MM-Conv benchmark this method improves grounding performance by 11-22 percentage points on average and enables a pure detector to reach 56.7% accuracy on pronominal references, nearly twice the best baseline result.

What carries the argument

Contextual rewriting of referring expressions using dialogue history to disambiguate before visual grounding

Load-bearing premise

The manually verified referring expressions from the VR-collected streams represent the distribution of ambiguity in spontaneous real-world conversations.

What would settle it

An experiment where the rewriting method is tested on conversational data collected outside of VR, such as from real human interactions in physical spaces, showing whether the performance improvements hold.

Figures

Figures reproduced from arXiv: 2605.21796 by Anna Deichler, Anna Klezovich, Fethiye Irmak Dogan, Iolanda Leite, Jim O'Regan, Jonas Beskow, Lubos Marcinek.

Figure 1
Figure 1. Figure 1: The multimodal data collection environ￾ment. A participant in a full-body motion capture suit and VR headset (left) interacts within a virtual scene. Their actions are rendered in real-time in the AI2-THOR simulator (right), enabling the synchro￾nized capture of egocentric vision, speech, motion, and 3D scene geometry. mocap-gaze/face-simulation). The timecode stream was also recorded inside the simulator … view at source ↗
Figure 2
Figure 2. Figure 2: A grounded data sample from the bench￾mark. For a referring expression like "box", our dataset provides synchronized data streams: (a) The egocentric RGB view with the ground-truth ref￾erent (box) highlighted by a green segmentation mask. (b) The raw RGB image. (c) The correspond￾ing depth map. This structure facilitates evaluation on precise, pixel-level grounding. 3.4. Dataset statistics Our dataset comp… view at source ↗
Figure 3
Figure 3. Figure 3: The interface for our human evaluation study. Crowd-workers were presented with an ego￾centric image and a corresponding utterance from the dataset. They were tasked with clicking on the object being referred to, providing a human base￾line for reference resolution. Each of the 1940 stimuli was evaluated by three participants. The total number of participants in￾cluded in the analysis, after discarding tho… view at source ↗
Figure 4
Figure 4. Figure 4: Object category distribution across simu [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Label Studio interface for speech annota [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

Grounding language in the physical world requires AI systems to interpret references that emerge dynamically during conversation. While current vision-language models (VLMs) excel at static image tasks, they struggle to resolve ambiguous expressions in spontaneous, multi-turn dialogue. We address this gap by introducing (1) a benchmark for referential communication in dynamic 3D environments, built from 6.7 hours of egocentric VR interaction with synchronized speech, motion, gaze, and 3D scene geometry, and (2) a two-stage grounding pipeline that explicitly resolves conversational ambiguity before visual localization. The benchmark includes over 4,200 manually verified referring expressions spanning full, partitive, and pronominal types. Our contextual rewriting approach improves grounding performance by 11-22 percentage points on average, with a pure detector (GroundingDINO) reaching 56.7% on pronominals after rewriting, nearly double the best end-to-end baseline. Results demonstrate that decoupling linguistic reasoning from visual perception is more effective than end-to-end approaches for conversational grounding.

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 / 2 minor

Summary. The manuscript introduces MM-Conv, a multimodal dataset and benchmark for context-aware grounding in 3D dialogue, constructed from 6.7 hours of egocentric VR interactions with synchronized speech, motion, gaze, and 3D scene geometry. It contains over 4,200 manually verified referring expressions spanning full, partitive, and pronominal types. The authors present a two-stage grounding pipeline that performs explicit contextual rewriting to resolve conversational ambiguity before applying visual localization, reporting average improvements of 11-22 percentage points over baselines, with a pure detector (GroundingDINO) reaching 56.7% accuracy on pronominals after rewriting—nearly double the best end-to-end baseline. The central claim is that decoupling linguistic reasoning from visual perception outperforms end-to-end approaches for conversational grounding.

Significance. If the reported gains are shown to arise from controlled comparisons rather than differences in context provision, the work supplies a new benchmark and empirical support for explicit linguistic resolution in dynamic multimodal settings. The combination of VR-collected multimodal streams and manual verification of referring expressions offers a concrete resource for advancing research on referential communication in embodied dialogue systems.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Experiments): The headline result that contextual rewriting yields 11-22 pp gains and enables GroundingDINO to reach 56.7% on pronominals requires explicit confirmation that the end-to-end baselines were supplied with the identical dialogue history, speech, gaze, motion, and 3D geometry streams used by the two-stage pipeline. The current description does not rule out the possibility that baselines were run as single-turn models or with truncated context, which would confound the attribution of gains to decoupling rather than to the simple addition of conversational context.
  2. [§3] §3 (Benchmark construction): The claim that the 4,200 manually verified expressions faithfully capture spontaneous conversational ambiguity rests on the VR collection protocol, yet no quantitative comparison is provided between the collected distribution and real-world dialogue corpora (e.g., frequency of pronominal references or ambiguity types). This weakens the generalizability argument for the benchmark.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'nearly double the best end-to-end baseline' should be accompanied by the exact baseline score for direct comparison.
  2. [§4] Figure captions and §4: Ensure all reported metrics include standard deviations or statistical significance tests to support the numeric gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We address each point below and commit to revisions that clarify the experimental controls and strengthen the discussion of benchmark representativeness.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): The headline result that contextual rewriting yields 11-22 pp gains and enables GroundingDINO to reach 56.7% on pronominals requires explicit confirmation that the end-to-end baselines were supplied with the identical dialogue history, speech, gaze, motion, and 3D geometry streams used by the two-stage pipeline. The current description does not rule out the possibility that baselines were run as single-turn models or with truncated context, which would confound the attribution of gains to decoupling rather than to the simple addition of conversational context.

    Authors: We appreciate this clarification request. In the experiments of §4, all methods—including the end-to-end baselines—were provided with the complete multimodal context consisting of the full dialogue history together with the synchronized speech, gaze, motion, and 3D geometry streams. The performance difference arises from the insertion of an explicit contextual rewriting stage in the two-stage pipeline versus direct end-to-end processing of the same contextual inputs. To remove any potential ambiguity, we will revise §4 (and update the abstract if needed) to state explicitly that identical input streams were used across all compared approaches. This change will better substantiate that the reported gains are attributable to the decoupling of linguistic resolution from visual grounding. revision: yes

  2. Referee: [§3] §3 (Benchmark construction): The claim that the 4,200 manually verified expressions faithfully capture spontaneous conversational ambiguity rests on the VR collection protocol, yet no quantitative comparison is provided between the collected distribution and real-world dialogue corpora (e.g., frequency of pronominal references or ambiguity types). This weakens the generalizability argument for the benchmark.

    Authors: We agree that a quantitative comparison would strengthen claims about how well the benchmark reflects real-world conversational patterns. The VR protocol was designed to elicit spontaneous multi-turn dialogue in a dynamic embodied setting, and the manual verification step ensures high fidelity of the referring expressions. The current manuscript does not contain such a distributional comparison. In the revised version we will expand §3 with available statistics on the frequency of full, partitive, and pronominal references in MM-Conv and include a discussion relating these figures to patterns reported in established dialogue corpora, while acknowledging limitations arising from the controlled VR environment. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark and performance comparison on newly collected data

full rationale

The paper collects a new multimodal VR dataset with synchronized streams and manually verifies 4,200 referring expressions, then measures grounding accuracy for a contextual rewriting pipeline versus end-to-end baselines. Reported gains (11-22 pp) are direct empirical deltas on this held-out benchmark; no equations, fitted parameters, or self-citations are invoked to derive the improvements. The central claim that decoupling linguistic resolution improves results is a falsifiable experimental outcome on released data rather than a self-referential definition or imported uniqueness theorem. Any concern about baseline context equivalence is an experimental-control issue, not a circularity reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work relies on standard data collection in VR and off-the-shelf grounding models.

pith-pipeline@v0.9.0 · 5743 in / 1202 out tokens · 33896 ms · 2026-05-22T08:30:24.836374+00:00 · methodology

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · 2 internal anchors

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    MM-Conv: A Multimodal Dataset and Benchmark for Context-Aware Grounding in 3D Dialogue

    Introduction Understanding and resolving referring expressions in situated, real-world contexts remains a core chal- lenge for multimodal AI. While recent progress in vision-language models (VLMs) has brought signif- icant advances in grounding natural language to images and videos, these models often fall short when it comes to reference resolution in dy...

    work page internal anchor Pith review Pith/arXiv arXiv 2017

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    Related Work 2.1. Referential Understanding Benchmarks Foundational datasets like ScanRefer (Chen et al., 2020)andReferIt3D(Achlioptasetal.,2020)estab- lished 3D referring expression grounding but rely on single-turn, text-only descriptions. While YouRe- fIt(Chenetal.,2021)addedmulti-turndialoguewith gesture, it uses third-person video. TEACh (Pad- makuma...

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    location tokens

    Dataset To create a benchmark for spontaneous, multi- modal referential grounding, we collected a new dataset consisting of 6.7 hours of interaction data recorded during a referential communication task in a virtual environment. Our primary goal was to capture the richness of embodied dialogue, includ- ing synchronized speech, full-body motion, gaze, and ...

    work page 2017

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    Human studies serve not only to benchmark model perfor- mance but also to validate the interpretability of our dataset

    Experiments To gain a comprehensive understanding of referen- tial grounding, we adopt a dual evaluation strategy combining crowd-sourced human judgments and vision-language model (VLM) evaluation. Human studies serve not only to benchmark model perfor- mance but also to validate the interpretability of our dataset. However, it is important to note that c...

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    Exact noun phrases:Explicit object names resembling classic referring expression bench- marks, where grounding depends on direct lexical match

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    the area,

    Filteredpartitives:PartitiveNPswithabstract or spatial terms (e.g., “the area,” “there”) re- moved, retaining only those grounded in iden- tifiable scene elements

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    it” or “that

    Subsampled pronominals:A representative subset of pronouns requiring discourse or vi- sual context for correct resolution. Evaluation Metrics.We evaluate visual ground- ing using Intersection over Union (IoU) between predicted and ground-truth bounding boxes, with ground-truth boxes derived from instance segmen- tation masks. We report accuracy at two sta...

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    This enables analysis of ground- ing in realistic, multimodal contexts beyond prior datasets

    Discussion and conclusions In this work, we present a multimodal benchmark for situated referential communication, combining spontaneous VR dialogue with synchronized 3D scene data. This enables analysis of ground- ing in realistic, multimodal contexts beyond prior datasets. Ahumantext-onlyevaluationestablishedalower bound for grounding performance. Parti...

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    Bibliographical References Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, and Leonidas Guibas

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    ReferIt3D:Neurallistenersforfine-grained 3D object identification in real-world scenes. InComputer Vision–ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pages 422–440. Springer. S. Bai. 2025. Qwen2.5-vl technical report.arXiv. Max Bain, Jaesung Huh, Tengda Han, and Andrew Zisserman. 2023. WhisperX: Time-acc...

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    Asking follow-up clarifications to resolve ambiguities in human-robot conversation. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 461– 469. Stephanie Gross, Brigitte Krenn, and Matthias Scheutz. 2017. The reliability of non-verbal cues for situated reference resolution and their inter- play with language: implicati...

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    Groundinggpt: Language-enhanced multi- modal grounding model (code/models).https: //github.com/lzw-lzw/GroundingGPT. MANUS. 2023. MANUS quantum meta- gloves. https://www.manus-meta.com/ products/quantum-mocap-metagloves. OptiTrack. 2023. Optitrack motion capture system. https://optitrack.com/. Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yuma...

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    the d e s k w i t h the l a p t o p

    Add s p a t i a l / r e l a t i o n a l c o n t e x t w h e n h e l p f u l ( e . g . , " the d e s k w i t h the l a p t o p " vs j u s t " the d e s k ")

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    T h e r e is no X v i s i b l e

    N E V E R o u t p u t : " T h e r e is no X v i s i b l e " , " The s c e n e d o e s not c o n t a i n " , " I c a n n o t see "

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    the s t o v e

    K e e p it c o n c i s e but s p e c i f i c ( t y p i c a l l y 3 -8 w o r d s ) E X A M P L E S : - " the s t o v e " + v i s i b l e : [ F i r e P l a c e ] - > " the f i r e p l a c e " - " it " + c o n t e x t a b o u t p a i n t i n g + v i s i b l e : [ P a i n t i n g 2 ] - > " the w a l l p a i n t i n g " - " the l i t t l e b l a c k t h i n g ...

    work page