{"id":"e2bef942-a7be-44f6-8e29-b138f6de4df1","arxiv_id":"2508.00428","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"A visual prompt engineering system for text-to-3D generation uses multi-view MLLM scoring and interactive visualizations to help designers create models faster, with 70.5% time reduction and higher quality ratings (4.58 vs 2.46).","lead":"Sel3DCraft is a visual prompt engineering system that helps people create 3D models from text by exploring candidate models, getting automatic feedback, and refining their prompts through visual tools. In a user study, designers created models about 70% faster than with standard systems, suggesting such guided workflows could make text-to-3D generation more practical.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Abstract claims 'significantly higher quality' while the contributions say 'comparable quality,' and the validation (Secs. 6–7) is unreadable in this artifact; the 70.5% speedup and 4.58 rating therefore cannot be traced to the MLLM scoring.","rationale":"I agree in part with the reader's weakest assumption: the MLLM-to-human alignment is indeed central and untested in the available text. I add two observations the reader did not emphasize: the abstract and the contributions make conflicting quality claims, and the evidence sections are absent from this artifact. The internal inconsistency is important because it directly affects the magnitude and attribution of the headline numbers. I do not treat the corruption as author misconduct; it is a property of the reviewed copy. The concrete test would resolve whether the concern lands: if a clean version shows a strong, significant MLLM-human correlation and a consistent baseline, the central claim becomes credible; if not, the quality and efficiency claims should be downgraded. Since either outcome is possible from the readable text, the appropriate verdict remains UNVERDICTED as the reader stated.","tokens_in":11765,"tokens_out":5690,"duration_ms":50797,"concrete_test":"Recover the clean full text and perform one verification: in Section 6, extract the table or figure correlating MLLM hybrid scores with independent human-expert quality ratings on held-out multi-view models. If the sample size is below 20 or the rank correlation is not reported/significant, the 'human-expert consistency' premise is unsupported. In the same pass, confirm that the Fig. 8 quality ratings and the contribution's 'comparable quality' statement refer to the same baseline condition; if they do not, the abstract's 'significantly higher' wording must be revised.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Two load-bearing problems block the central claim that Sel3DCraft clearly outperforms other T23D systems. First, the abstract reports 'significantly higher model quality ratings (4.58 vs 2.46, Fig. 8)' while the third contribution says the system 'achieves faster model creation while maintaining comparable quality.' These describe different outcomes; if Fig. 8's comparison is not against the same baseline, the reported quality advantage is ambiguous. Second, the validation this claim depends on is not inspectable here: Section 6 (validation of the MLLM scoring) and Section 7 (user study) are corrupted/unreadable, so one cannot check whether the MLLM hybrid scores actually correlate with human-expert judgment, or whether the 70.5% time reduction and 66.2% iteration reduction are confounded by the retrieval branch or by task design. The load-bearing assumption is therefore that the MLLM scoring is human-aligned and that the user-study measurements are causally attributable to the scoring-driven recommendation loop. Neither can be verified from the provided artifact.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces Sel3DCraft, an interactive visual prompt engineering system for text-to-3D (T23D) generation. The system combines a dual retrieval/generation branch, MLLM-based multi-view hybrid scoring, and a visual analytics suite (multi-view satellite charts, hybrid-level scoring heatmaps, and a prompt-driven treemap wordle) to guide users from unstructured prompt trial-and-error to structured exploration. The abstract claims that user studies show a 70.5% reduction in model creation time, a 66.2% reduction in prompt iterations, and significantly higher model quality ratings (4.58 vs 2.46, Fig. 8), concluding that Sel3DCraft surpasses other T23D systems in supporting designer creativity.","tokens_in":11930,"tokens_out":1621,"duration_ms":16695,"significance":"If the reported results are reproducible and the MLLM scoring genuinely aligns with human expert judgment, the system would be a meaningful contribution to visual analytics and creativity support for 3D content creation. The proposed cross-modal visual representation and the focus on multi-view consistency are timely. However, the provided manuscript text is largely unreadable due to encoding corruption, and the internal claims about quality are inconsistent. The significance therefore hinges entirely on material that cannot currently be inspected.","major_comments":[{"comment":"The abstract states that Sel3DCraft yields 'significantly higher model quality ratings (4.58 vs 2.46, Fig. 8)', while the third listed contribution states that the system 'achieves faster model creation while maintaining comparable quality.' These statements describe different outcomes for the same comparison. The manuscript must specify which baseline each figure refers to, report the full statistical details (sample size, test, effect size, confidence intervals), and reconcile the two phrasings; otherwise the central quality advantage is ambiguous.","section":"Abstract / Contributions"},{"comment":"The validation of the MLLM hybrid scoring (Section 6) and the user study (Section 7) are unreadable in the provided version of the manuscript because the text is corrupted (repeated glyph placeholders replace the actual prose). These sections are load-bearing for every quantitative claim in the abstract, including the 70.5% time reduction, the 66.2% iteration reduction, and the 4.58 vs 2.46 quality ratings. A readable, complete version of these sections with the full experimental protocol, baseline definitions, participant demographics, and raw measurements must be provided before the central claims can be assessed.","section":"Sections 6–7"},{"comment":"The multi-view hybrid scoring approach relies on the assumption that MLLM-based high-level metrics correlate with human expert judgment of 3D model quality. Because the validation section is unreadable, there is no evidence in the accessible text that this assumption holds. The paper should provide explicit correlation or agreement measures between MLLM scores and human ratings, and should ablate the contribution of the scoring loop versus the retrieval branch versus the generation branch to the reported time and iteration reductions.","section":"Sec. 4.2 / Abstract"}],"minor_comments":[{"comment":"Large portions of the text, including in-text citations and reference titles, are corrupted with placeholder glyphs, making it impossible to read the full methodology and related work discussion. The authors should ensure a correctly encoded PDF is submitted.","section":"Throughout"},{"comment":"Several reference entries have corrupted titles and venue names (e.g., references [1], [4], [5], [10]). These should be cleaned up so that readers can locate the cited works.","section":"References"},{"comment":"The abstract cites Fig. 8 for the quality ratings, but the figure is not viewable in the provided artifact. Please ensure all figures are embedded correctly and referenced consistently.","section":"Fig. 8"}],"recommendation":"major_revision","confidential_remarks":"The core issue is not the technical idea but the unverifiability of the provided artifact. The encoding corruption affects exactly the sections that contain the evaluation, and the abstract/contribution inconsistency compounds the problem. I would encourage the editor to request a clean, complete manuscript and a point-by-point reconciliation of the quality claims before sending this out for further review."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThis paper presents Sel3DCraft, a visual prompt engineering system for text-to-3D generation. The core idea is sensible: give designers a dual-branch retrieval/generation interface, use an MLLM to score multi-view renders of candidate 3D models, and provide a visual analytics dashboard for defect identification and prompt refinement. That is a useful direction, and the related work is well chosen. The paper cites the obvious prior art, including GPT-4V as a human-aligned evaluator for text-to-3D (ref [60]), which makes the \"for the first time\" claim in the contributions inaccurate. What is likely new is the integration of MLLM scoring with an interactive visual analytics loop for 3D, not the MLLM evaluation itself.\n\nThe stress-test note is right about a real internal contradiction. The abstract says the system achieves \"significantly higher model quality ratings (4.58 vs 2.46)\" while the third contribution says it \"achieves faster model creation while maintaining comparable quality.\" Those are different claims. If the user study compared against different baselines, the quality gap could be misleading. This needs to be fixed in revision.\n\nThe bigger problem is that the artifact I have is largely corrupted beyond the introduction and references. Sections 4-7, which contain the method, the MLLM scoring validation, and the user study, are unreadable. So I cannot trace the 70.5% speedup, the 66.2% iteration reduction, or the quality ratings to their experimental derivation. It is possible the full paper is solid, but from this version I can verify none of the quantitative claims. I also cannot check whether the MLLM hybrid scores correlate with human expert judgment, which is the load-bearing assumption for the recommendation loop. The self-referential nature of using an MLLM to evaluate candidates is not fatal on its own, because the user study would provide external grounding, but that grounding is invisible here.\n\nIf the completed version actually contains the experiments, this could be a legitimate VIS contribution. The system concept is timely, and the visual analytics components are described with enough specificity to be interesting. But as it stands, the paper has an internal quality-claim contradiction and an unverifiable evaluation.\n\nFor peer review: yes, a serious editor should send this to reviewers. The idea is important enough and the claimed effect sizes are large enough to deserve referee time. The authors will need to address the contradiction and make the evaluation readable. I would not cite it in my own work until I see the full version.\n\nRegards.","headline":"A promising system idea with an internal quality-claim contradiction and unreadable validation sections in this artifact; worth refereeing on the strength of the concept, but the claims as stated are unverifiable.","tokens_in":12527,"tokens_out":2192,"would_cite":false,"duration_ms":19590,"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":"Interactive visual prompts replace blind trial-and-error in text-to-3D design.","keywords":["text-to-3D generation","visual prompt engineering","multi-modal language models","visual analytics","prompt recommendation","multi-view consistency","creativity support","interactive systems"],"falsifier":"Re-run the user study with the MLLM scoring replaced by random scores while keeping everything else identical; if time-to-completion and quality ratings stay about the same, the scoring is not the cause of the reported gains. Alternatively, compare MLLM per-view scores with independent human expert ratings on the same candidate set; a correlation near zero would falsify the claim that the scoring matches human-expert consistency.","tokens_in":11534,"feed_emoji":"🎨","tokens_out":6400,"duration_ms":56355,"temperature":0.7,"pith_summary":"Text-to-3D generation is fast but unpredictable: users type a description, wait through a black-box process, and often get a defective model that needs many blind retries. The paper claims that this trial-and-error loop can be replaced by a guided visual workflow. It introduces Sel3DCraft, an interactive system that proposes candidate models through both retrieval and generation, scores them from multiple rendered views using multimodal large language models, and visualizes scores and defects so users can click on a problem and get a prompt edit. In a user study, designers finished models in 118.83 seconds instead of 402.17 seconds, a 70.5% reduction, with 66.2% fewer prompt iterations and a rated-quality jump from 2.46 to 4.58 out of five. If the results hold, the system turns text-to-3D from a lottery into a designer-controlled process.","feed_headline":"A visual prompt system cuts 3D model creation time by 70.5%","feed_subtitle":"Sel3DCraft adds multi-view scoring and visual analytics to text-to-3D, so designers iterate 66% fewer times.","key_machinery":"The central mechanism is multi-view hybrid scoring, which renders each candidate model from multiple viewpoints and asks a multimodal large language model to produce high-level quality metrics for each view and aspect. These scores serve two purposes: they cluster similar candidates in a satellite-chart view for quick exploration, and they feed a per-view heatmap that localizes defects such as texture tears or geometry glitches. The second essential piece is the prompt-driven treemap wordle, an image-text matching visualization that links semantic keywords to the multi-view images, so a user can click a suggested word to refine the prompt. Together, the scoring loop and the wordle convert an unstructured search over prompts into a visible cycle of evaluate-defect-recommend-refine.","core_discovery":"The central claim is that the whole interaction loop — dual-branch candidate synthesis, MLLM-based multi-view scoring, and prompt-driven visual analytics — produces the measured gains, not any single generation model in isolation. The paper argues that rendering each candidate from several views and feeding those images to a multimodal large language model yields high-level quality metrics, such as structural integrity and cross-view coherence, that track what human experts would say. Those scores drive a satellite-chart clustering view and a per-view heatmap that localize defects, while a treemap wordle maps visual defects back to prompt keywords. Closing the loop lets a designer refine a prompt by clicking on words rather than rewriting entire descriptions. The reported outcome is faster model creation, fewer iterations, and higher rated quality than existing text-to-3D systems.","pith_inferences":["Because the reported gains are for the full system, the paper does not isolate how much each component contributes; a plausible open question is whether MLLM scoring alone would already provide most of the time savings.","The user study numbers come from a specific set of designers and prompts; repeating the study with novice users or with a different base generator would clarify how much of the benefit is interaction design versus the underlying generation speed.","One testable extension is to detach the scoring module from the interface and use it to rank prompts offline, which would let the system suggest entire prompt variants rather than single keywords."],"forward_implications":["If the interaction loop works as reported, professional 3D design tools can move from blind prompt retries to a guided evaluate-and-refine cycle, cutting the per-model cost dramatically.","The MLLM-based multi-view scoring could be reused outside the system as a reference-free evaluation metric for text-to-3D output quality, giving researchers a proxy that tracks human judgment.","Mixing retrieval from existing 3D assets with freshly generated candidates is a cheap way to broaden exploration without extra generation cost, a design pattern other interfaces could adopt.","The defect-to-keyword mapping embodied in the wordle could be applied to text-to-image and other generative model interfaces, making visual analytics a standard part of prompt engineering."],"supporting_citations":[{"why":"Supplies the prior visual prompt engineering approach that this paper extends from 2D to 3D.","marker":"[13]"},{"why":"Provides the single-image-to-3D reconstruction backbone that anchors the generation branch.","marker":"[18]"},{"why":"Offers fast multi-view text-to-3D generation used to produce initial candidate models.","marker":"[23]"},{"why":"Powers the retrieval branch with a text-image-3D pretrained model for diverse shape exploration.","marker":"[26]"},{"why":"Provides the image-text matching model behind the wordle that links prompt keywords to multi-view images.","marker":"[22]"},{"why":"Supports the premise that multimodal LLMs can evaluate text-to-3D quality in line with human judgment.","marker":"[60]"},{"why":"Underpins the MLLM-as-judge assumption used in the multi-view hybrid scoring module.","marker":"[7]"}],"fun_headline_variants":["Visual prompts turn text-to-3D from blind trial into guided clicks","Multi-view AI scoring makes text-to-3D refinement visual and fast","Click words, not prompts: Sel3DCraft guides 3D design visually"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The assumption that the MLLM's multi-view quality scores agree with what human experts consider good 3D models; if the scores are out of line, the prompt recommendations can lead users to models that score well but are not actually better.","fun_headline_variants_meta":{"raw":{"variants":["Visual prompts turn text-to-3D from blind trial into guided clicks","Multi-view AI scoring makes text-to-3D refinement visual and fast","Click words, not prompts: Sel3DCraft guides 3D design visually"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000745,"raw_usage":{"total_tokens":3283,"prompt_tokens":867,"completion_tokens":2416,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":483,"completion_tokens_details":{"reasoning_tokens":2352}},"tokens_in":483,"tokens_out":2416,"duration_ms":13535,"temperature":1.0,"reasoning_tokens":2352,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T10:07:49.264416+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the user study with the MLLM scoring replaced by random scores while keeping everything else identical; if time-to-completion and quality ratings stay about the same, the scoring is not the cause of the reported gains. Alternatively, compare MLLM per-view scores with independent human expert ratings on the same candidate set; a correlation near zero would falsify the claim that the scoring matches human-expert consistency.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the single-image-to-3D reconstruction backbone that anchors the generation branch."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Offers fast multi-view text-to-3D generation used to produce initial candidate models."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Powers the retrieval branch with a text-image-3D pretrained model for diverse shape exploration."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the image-text matching model behind the wordle that links prompt keywords to multi-view images."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Underpins the MLLM-as-judge assumption used in the multi-view hybrid scoring module."}],"review_version":1}