{"id":"e6eefe1a-a8ca-42c4-a964-c6f606c0d85d","arxiv_id":"2508.00427","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":2,"one_line_summary":"A contact-aware, multi-regional inpainting method using a pretrained diffusion model completes occluded objects in human-object interactions without additional training.","lead":"This paper presents a training-free method to infer the full appearance of objects hidden during human-object interactions, using contact points to define where to inpaint and a pretrained diffusion model to fill in the missing regions. The authors report that this approach beats existing amodal completion methods on HOI images and supports 3D reconstruction and novel view synthesis.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Primary-region coverage assumption is unvalidated and can fail when occluded object parts lie outside the convex hull of contact points.","rationale":"The reader correctly identified the convex-hull assumption as the weakest load-bearing assumption, and I agree that it is central. My critique adds specificity: the failure is not just geometric but also arises from the interaction with contact estimation noise, which the paper explicitly claims to be robust against. Because the submitted content lacks the method section, ablation studies, and quantitative results, the claim remains unverified, and the reader's UNVERDICTED verdict is appropriate. The proposed concrete test would directly measure whether Mp covers the occluded object pixels, which is the minimal condition for the multi-regional inpainting to have any chance of success. If that test passes, the central claim could still hold, but without it the approach rests on an unproven geometric prior.","tokens_in":6018,"tokens_out":1831,"duration_ms":20758,"concrete_test":"Run the proposed region identification on BEHAVE or a synthetic HOI dataset with ground-truth amodal masks. For each image, compute the recall of the ground-truth occluded object mask inside the primary region Mp, using (a) ground-truth contact points and (b) estimated contacts from a pretrained detector. If recall is below 0.9 for more than 10% of frames, or if the estimated-contact recall drops by more than 5 percentage points relative to ground-truth contacts, then the primary-region assumption fails and the central performance claim is not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The pipeline's core is the claim that the convex-hull-based primary region Mp is 'highly likely to contain occluded parts' (Figure 1). The central claim of substantial outperformance requires Mp to actually contain the missing object area, because the customized denoising concentrates reconstruction effort there while the secondary region receives weaker handling. Two concrete failure modes exist. First, for objects such as long-handled tools, a hand contact point may lie on the handle while the blade extends behind the body; the convex hull of contact points plus the human-object boundary can exclude that far occluded portion, so the completion never generates it. Second, the paper emphasizes robustness without ground-truth contact annotations, but estimated contact points (e.g., from CONTHO, HOT, or DECO) are noisy; small shifts can move the hull enough to exclude true occluded pixels. The manuscript provides no quantitative evidence about Mp coverage, no ablation on contact estimation error, and no sensitivity analysis. Since the submitted text omits the method section and experiments, this load-bearing geometric assumption is entirely unsupported.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a training-free amodal completion pipeline for human-object interaction (HOI) that decomposes the occluder region into a contact-derived primary region and a secondary region, then applies customized denoising strategies within a pre-trained diffusion model. The authors claim that this multi-regional inpainting method substantially outperforms existing methods in HOI scenarios, remains robust without ground-truth contact annotations, and benefits downstream applications such as 3D reconstruction and novel-view synthesis. However, the submitted manuscript omits the method details, equations, and experimental results, so the central claims cannot be verified from the available text.","tokens_in":6197,"tokens_out":2720,"duration_ms":28994,"significance":"If the claims are substantiated, the work would provide a training-free, physically motivated alternative to single-mask diffusion inpainting for HOI, and the multi-region denoising idea could be useful beyond this specific setting. The paper also names concrete practical applications (Gaussian Splatting reconstruction, novel-view synthesis) and includes a user-study appendix, which indicates awareness of evaluation pitfalls. That said, the significance is entirely contingent on the missing technical and experimental content; as submitted, the paper is an extended abstract rather than a complete journal contribution. The paper ships no machine-checked proofs, no reproducible code, and no numerical results, so its strengths currently lie only in the plausibility of the proposed region-decomposition concept.","major_comments":[{"comment":"The manuscript contains only an abstract, introduction, related work, references, and appendix figures; there is no method section, no equations, no algorithm pseudocode, and no experiments section with quantitative results. The abstract's claim that the approach 'substantially outperforms existing methods' is therefore unsupported, and the central contribution cannot be evaluated. A complete version with the full method and results is required before the paper can be considered.","section":"Overall manuscript structure"},{"comment":"The load-bearing geometric assumption is that the convex hull of contact points and the human-object boundary reliably identifies the primary region that contains the occluded object parts. The paper provides no validation of this assumption, and it has plausible failure modes: for long-handled tools the occluded far end can lie outside the hull, and estimated contact points from methods such as CONTHO, HOT, or DECO are noisy. Without coverage statistics, ablations, or sensitivity analysis with respect to contact estimation error, the proposed region definition is not established.","section":"Figure 1 and Section 1"},{"comment":"The user study section describes the protocol (223 sample pairs, average of 10 users each) but reports no results. Since the paper explicitly acknowledges that CLIP score and mIoU are limited, the user study outcomes are essential evidence for the visual-quality claim; the results, including agreement rates or preference percentages, must be reported.","section":"Appendix C.3"},{"comment":"The claim that the pipeline 'remains robust even without ground-truth contact annotations' is not backed by any experiment in the submitted text. The paper should show quantitative results comparing contact sources or demonstrating performance with estimated contacts, and should also report a failure analysis; Figure 13 lists failure categories (orientation, shape, segmentation) but no analysis is provided.","section":"Abstract and Section 1"},{"comment":"The contribution statement claims this is the first work to address amodal completion in HOI, but the related work already cites diffusion-based amodal completion methods and HOI contact estimators. The novelty claim needs to be sharpened: the distinguishing factor appears to be the multi-region mask and denoising strategy, not the overall problem formulation, and this should be stated precisely.","section":"Section 2"}],"minor_comments":[{"comment":"Several reference entries contain stray trailing numbers or inconsistent formatting, for example entries that end with '5, 3' or similar; the reference list should be cleaned and standardized.","section":"References"},{"comment":"The subfigure labels in Figure 1 jump from (b) to (e), skipping (c) and (d); the labels should be fixed or the omitted subfigures should be included.","section":"Figure 1"},{"comment":"Figures 11-15 are referenced in the appendix but do not appear to have corresponding callouts in the main text; the narrative should integrate these figures with in-text mentions.","section":"Appendix figures"},{"comment":"The phrase 'we've developed' in the abstract is informal for a journal paper; consider replacing it with 'we develop' or 'we propose'.","section":"Abstract"},{"comment":"The manuscript is internally inconsistent in length and completeness: the introduction promises a full framework, but the visible content stops after related work. The authors should ensure the submitted file includes all sections.","section":"Overall"}],"recommendation":"major_revision","confidential_remarks":"The submission appears to be an incomplete version: the method and experiments sections are absent, and the appendix contains only figures without results. I recommend that the editor require a complete manuscript before further review; if a complete version is not available, the paper would have to be rejected rather than conditionally accepted."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things to know. First, the core idea is genuinely new and sensible: split the occluder region into a contact-derived convex hull (primary) and the surrounding area (secondary), then apply differentiated denoising to each in a training-free diffusion pipeline. That is a clean way to address a real failure mode in HOI, where the visible occluder area is much larger than the actual hidden object. Second, the version we have contains no method section, no equations, and no quantitative results. The abstract says it 'substantially outperforms' existing methods, but the only evidence in the supplied text is a mention of a user study and some appendix figure captions. So the central claim is, as of this version, unsupported.\n\nWhat the paper does well: the related work is well positioned against pix2gestalt and progressive mixed context diffusion, and the authors correctly identify why those methods struggle in HOI: the occluder region is over-broad, so whole-mask inpainting distorts the scene. The two-region decomposition with different denoising priorities is a plausible remedy, and the downstream demonstration on 3D Gaussian Splatting is a good instinct, even if we cannot see the numbers. The appendix suggests the authors ran a user study and analyzed failure cases, so the evaluation is not fabricated; it is just absent from this excerpt.\n\nThe soft spots are real but concentrated. The load-bearing geometric assumption is that the convex hull of contact points plus the human-object boundary will contain the hidden object parts. For a long tool, the blade can extend behind the torso and outside that hull; estimated contacts are also noisy, and small shifts can move the hull. The paper provides no coverage statistics, no ablation on contact-estimation error, and no sensitivity analysis. That is a serious gap, because the whole method rests on the primary region actually containing the missing object. The description of the user study is also too thin to judge: no statistics, no inter-rater measures, just counts.\n\nAs a submission artifact, this looks like an incomplete preprint rather than a defective one. The conceptual framing is coherent and the authors are clearly engaging with the right literature. If the full paper supplies the method details and quantitative comparisons, it deserves a serious referee. The hull-coverage concern needs to be addressed head-on; if it is not, the paper will need major revision even after those additions. I would not desk-reject this idea, and I would not cite it yet. Bring it to reading group only if the full version is available.","headline":"A sensible two-region inpainting idea for HOI amodal completion, but this version lacks the method and experiments, leaving the central claim and the hull assumption unverified.","tokens_in":754,"tokens_out":777,"would_cite":false,"duration_ms":23750,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A training-free diffusion method completes occluded objects in human-object interactions by splitting the occluder into two priority regions.","keywords":["amodal completion","human-object interaction","diffusion model","image inpainting","contact estimation","convex hull","multi-regional inpainting","occlusion"],"falsifier":"Construct an HOI image where the occluded portion of an object falls outside the convex hull of the contact points and the human-object boundary—for instance, a person holding a long rod with the far end hidden behind their back—and measure whether the method's completed object recovers that outside-hull part. A systematic failure to reconstruct parts outside the primary region would falsify the core localization assumption.","tokens_in":5827,"feed_emoji":"🧩","tokens_out":7482,"duration_ms":57600,"temperature":0.7,"pith_summary":"The paper tackles amodal completion for human-object interaction: inferring the full shape of an object when a person occludes part of it. Existing diffusion-based inpainters often overextend or misplace the completed object because they treat the whole occluder as the region to fill. The authors propose to use physical priors—contact points between hand and object, and the human-object boundary—to split the occluder into a primary region, where the hidden object parts most likely sit, and a secondary region with lower probability. They then apply customized denoising strategies to each region inside a pre-trained latent diffusion model, without additional training. Their experiments show this two-region approach substantially outperforms existing single-region baselines in HOI scenarios, and remains effective even when contact annotations are predicted rather than ground-truth.","feed_headline":"Convex hull of contacts pinpoints hidden object parts","feed_subtitle":"A training-free diffusion method splits the occluder into two regions, improving amodal completion for human-object interaction.","key_machinery":"The key object is the two-region decomposition of the occluder: the primary region mask $M_p$, obtained by a convex hull over contact points and the human-object boundary, and the secondary region $M_s$ covering the rest of the occluder. This mask pair feeds a multi-regional inpainting procedure that runs a pre-trained latent diffusion model with different denoising strategies per region—coarse structure in $M_p$ and finer detail in $M_s$—so the completion is focused where the hidden object actually is.","core_discovery":"The central claim is that the occluded parts of an object during human-object interaction can be localized with a convex hull built from contact points and the human-object boundary, and that completing the image by inpainting this 'primary region' with coarse structure while adding finer detail in the 'secondary region' yields more accurate and realistic amodal completions than inpainting a single mask. The paper argues that the occluder region (the person) is typically much larger than the actual hidden object area, so targeting the inpainting prevents overextension. The method works by extending a pre-trained latent diffusion model with region-specific denoising schedules, requiring no training, and the authors demonstrate robustness when ground-truth contact annotations are replaced with predicted ones, enabling applications such as 3D reconstruction and novel-view/pose synthesis.","pith_inferences":["The convex-hull localization will likely miss occluded object parts that extend outside the hand-centered region, such as a long tool whose far end wraps behind the body; this is a testable failure mode for elongated or articulated objects.","The same two-region decomposition could transfer to other occlusion settings with available contact or boundary priors, including hand-object manipulation and animal-object interaction.","The customized denoising schedule might be reused for general image editing where a user specifies which mask areas require structural changes versus fine detail.","Pairing the method with a learned contact estimator would produce a fully automatic annotation-free amodal completion system, extending the paper's predicted-contact experiments."],"forward_implications":["Amodal completion for human-object interaction can be performed with any pre-trained diffusion inpainting model by supplying region-specific denoising schedules, with no additional training.","The pipeline remains effective when contact points come from an automatic predictor instead of ground-truth annotations, removing a manual labeling burden.","Amadally completed images improve downstream performance in 3D reconstruction with Gaussian Splatting and in novel-view and novel-pose synthesis.","Dividing the occluder into a primary and a secondary region avoids over-inpainting artifacts that occur when a single large mask is filled indiscriminately."],"supporting_citations":[{"why":"Provides the pre-trained latent diffusion model that the multi-regional inpainting extends without training.","marker":"[25]"},{"why":"A state-of-the-art amodal completion baseline that the method compares against and outperforms.","marker":"[22]"},{"why":"Another amodal completion baseline, representing the closest single-mask inpainting approach.","marker":"[37]"},{"why":"Supplies the BEHAVE dataset of human-object interaction images used for evaluation.","marker":"[2]"},{"why":"Provides the contact estimation method used to derive contact points for the primary-region hull.","marker":"[4]"},{"why":"An alternative contact estimator referenced for robustness experiments without ground-truth contacts.","marker":"[32]"},{"why":"Used to segment the human and object from the image before inpainting.","marker":"[24]"},{"why":"Enables the downstream 3D reconstruction application the paper demonstrates.","marker":"[12]"}],"fun_headline_variants":["Contact hull guides inpainting for hidden objects","Multi-region diffusion completes occluded objects","Training-free amodal completion via contact hull","Diffusion inpaints with contact-aware regions","Hidden object parts from contact convex hull"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that the occluded object parts lie inside the convex hull of the contact points and the human-object boundary; if an object's hidden part extends outside that hull (for example, a long-handled tool wrapped behind the body), the primary region will miss it and the completion will be wrong.","fun_headline_variants_meta":{"raw":{"variants":["Contact hull guides inpainting for hidden objects","Multi-region diffusion completes occluded objects","Training-free amodal completion via contact hull","Diffusion inpaints with contact-aware regions","Hidden object parts from contact convex hull"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000153,"raw_usage":{"total_tokens":1204,"prompt_tokens":936,"completion_tokens":268,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":552,"completion_tokens_details":{"reasoning_tokens":202}},"tokens_in":552,"tokens_out":268,"duration_ms":2900,"temperature":1.0,"reasoning_tokens":202,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T10:07:51.196916+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Construct an HOI image where the occluded portion of an object falls outside the convex hull of the contact points and the human-object boundary—for instance, a person holding a long rod with the far end hidden behind their back—and measure whether the method's completed object recovers that outside-hull part. A systematic failure to reconstruct parts outside the primary region would falsify the core localization assumption.","supporting_citations":[{"cited_title":"High-resolution image synthesis with latent diffusion models","cited_arxiv_id":null,"evidence_quote":"Provides the pre-trained latent diffusion model that the multi-regional inpainting extends without training."},{"cited_title":"pix2gestalt: Amodal segmentation by synthesizing wholes","cited_arxiv_id":null,"evidence_quote":"A state-of-the-art amodal completion baseline that the method compares against and outperforms."},{"cited_title":"Amodal com- pletion via progressive mixed context diffusion","cited_arxiv_id":null,"evidence_quote":"Another amodal completion baseline, representing the closest single-mask inpainting approach."},{"cited_title":"Behave: Dataset and method for tracking human object in- teractions","cited_arxiv_id":null,"evidence_quote":"Supplies the BEHAVE dataset of human-object interaction images used for evaluation."},{"cited_title":"Detecting human-object contact in images","cited_arxiv_id":null,"evidence_quote":"Provides the contact estimation method used to derive contact points for the primary-region hull."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"An alternative contact estimator referenced for robustness experiments without ground-truth contacts."},{"cited_title":"3d gaussian splatting for real-time radiance field rendering","cited_arxiv_id":null,"evidence_quote":"Enables the downstream 3D reconstruction application the paper demonstrates."}],"review_version":1}