{"id":"e26a2b58-ac7a-46c9-b667-243ed70ca4ab","arxiv_id":"2502.06860","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"A two-stage method that uses VLM-generated style descriptions and style-adjustment code to complete partial vector sketches in a style-consistent, prompt-aligned way.","lead":"AutoSketch completes a partial sketch by adding new strokes that match a text prompt while inheriting the original drawing's style. It uses a vision-language model to describe the sketch's style, then adjusts the new strokes with computer code generated by that same model.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Core style-adjustment stage (Section 5) is never validated in isolation; the central claim depends on unquantified VLM code-generation reliability, and the only supporting evidence is a small, unpowered user study.","rationale":"The reader's weakest assumption centered on Stage-1 VLM style descriptions being accurate enough to steer ControlNet. I find that concern less load-bearing because Stage 1's style augmentation only provides a prior; Stage 2 is the explicit mechanism designed to enforce style consistency on the newly generated strokes. The paper itself concedes Stage 2 involves an unstable VLM and occasional omissions, yet provides no isolated evaluation of this stage. The evaluation weaknesses (10 sketches, no significance testing, no code/data) are real but are issues of evidence quality; the specific technical concern about Stage 2 reliability is more directly load-bearing because if Stage 2 fails frequently, no re-running of the user study would salvage the claim. My proposed test would settle this by isolating the stage and measuring the actual style change it produces on a controlled set. The current evidence supports a conditional acceptance: the method is plausible and qualitatively promising, but the central style-consistency claim requires the proposed reliability check before it can be fully trusted.","tokens_in":14368,"tokens_out":7154,"duration_ms":77473,"concrete_test":"Construct 50 input partial sketches with distinctly different stroke statistics (e.g., width 8, opacity 1.0 vs. width 1, opacity 0.2) and, for each, generate an intermediate sketch from the first stage using a fixed augmentation. Run the Stage-2 style-adjustment procedure on each pair. Measure the stroke-width, opacity, and curvature distributions of the generated strokes before and after adjustment, and count how many cases shift significantly toward the input stroke statistics while preserving all content strokes. If the adjustment succeeds in fewer than, say, 80% of cases, the style-adjustment stage is too unreliable to support the central claim.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The method's central claim of style-consistent completion rests on the iterative VLM style-adjustment stage (Section 5). In each iteration, the VLM is asked to identify style differences between input strokes (blue) and generated strokes (black) from a rendered image plus SVG, and to emit executable code that adjusts the generated strokes toward the input style while preserving content. The paper's evidence for this stage's reliability is anecdotal: Figures 6 and 13 show one successful case, and the paper itself concedes the VLM 'occasionally overlooks some differences' and is 'inherently unstable' (Section 1). No failure rate, no ablation of Stage 2 alone, and no metric measuring style distance (e.g., stroke width/opacity/curvature distributions) between input and generated strokes is reported. The user study in Table 2 only tests the full pipeline on 10 sketches without significance testing, so a failure of Stage 2 could be masked by Stage 1's style augmentation or by participant bias toward overall aesthetics. If the VLM's adjustment code frequently fails to close the style gap (by leaving mismatched stroke attributes or by deleting strokes), the headline claim that AutoSketch 'completes the input sketch in a style-consistent manner' is not established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces AutoSketch, a two-stage method for completing partial vector sketches from a text prompt while preserving the style of the input. In the first stage, a vision-language model (VLM) produces natural-language style descriptions from the rasterized partial sketch; these are appended to the prompt and used by ControlNet to generate a non-photorealistic guidance image, against which a set of Bézier strokes is optimized using CLIP and LPIPS losses plus an overlap penalty. In the second stage, the VLM is prompted to detect style differences between input and generated strokes and to emit executable Python code that adjusts stroke attributes or removes strokes; this process is repeated until no further changes occur. The method is evaluated against SketchDreamer, DiffSketcher, and Gemini Co-Drawing on 10 sketches, with DreamSim/DINO/VQA metrics, pairwise user studies, and ablations of prompt augmentation, VLM choice, and the style-adjustment code.","tokens_in":14644,"tokens_out":6705,"duration_ms":65094,"significance":"The work addresses a genuine gap: existing sketch-generation methods create sketches from scratch and do not complete partial sketches in the style of the input. The two-stage design, particularly the use of a VLM to write executable adjustment code, is innovative and enables discrete style changes (stroke deletion, simplification) that continuous optimization cannot handle. The paper presents a plausible pipeline with extensive qualitative results, ablations, and a user study. If the reported quality holds at scale, the method would be a practical tool for iterative sketch creation. However, the quantitative evidence is currently too weak to fully support the headline claims: the evaluation set has only 10 items, no error bars or significance tests are reported, and the reliability of the VLM code-generation stage—which is the crux of style adjustment—is not directly measured. The central idea is defensible and interesting, and the main issues are fixable with additional evaluation and clarifications.","major_comments":[{"comment":"The quantitative evaluation is based on a single set of only 10 sketches, and no error bars, confidence intervals, or significance tests are reported. The text says the method 'significantly outperforms' the baselines (Section 6.2), but with n=10 and no variance estimates this claim is not supported. The user study in Table 2 reports preference percentages but no per-participant agreement, no test statistics, and no confidence intervals; with 25 participants, the observed margins (e.g., 76.79% vs. 23.21% in row c) may be within sampling noise. Please report per-item results, standard deviations (or confidence intervals), and appropriate tests (e.g., Wilcoxon signed-rank or bootstrap), and ideally increase the number of evaluation sketches.","section":"Section 6.2, Tables 1 and 2"},{"comment":"The central claim of style-consistent completion rests on the VLM-based adjustment stage, yet its reliability is not quantified. The paper itself concedes that the VLM 'occasionally overlooks some differences' (Section 3) and that the iterative process exists 'due to the inherent instability of the VLM' (Section 1). The only ablation of this stage (Section 6.4.1) compares the first-stage output with the full pipeline on aggregate metrics over 10 sketches; since DreamSim and DINO are content-style entangled and the differences are not tested for significance, this does not establish that Stage 2 consistently performs the intended adjustment. I recommend adding a direct evaluation of Stage 2: apply it to a set of style-transfer pairs with known ground-truth stroke attributes, report the success rate of the generated code (including cases where it deletes content or fails to modify anything), and report the distribution of the number of iterations required for convergence.","section":"Sections 5 and 6.4.1"},{"comment":"The overlap penalty term γ Σ 1[M(x_k)=1] is non-differentiable as written, since an indicator function has zero gradient almost everywhere. The paper does not specify how this term is incorporated into gradient-based optimization—whether via a smoothed approximation, a straight-through estimator, a barrier method, or a hard constraint. Without this detail, the optimization procedure is underspecified and the reported ability to avoid redundant strokes is not reproducible. Please clarify the implementation or provide a differentiable surrogate and state how gradients are obtained.","section":"Equation (2) and Section 4.2"},{"comment":"The paper correctly acknowledges that DreamSim and DINO measure both style and content and can favor leaving the rest of the sketch blank. Yet these are the only quantitative style-related metrics; the VQA score measures content alignment only. Consequently, the claim that the method 'better preserves the style' is supported only by the user study, which is small and untested. Please add a style-focused quantitative metric (e.g., distance in stroke width, opacity, smoothness, or curvature distributions between input and generated strokes, or a perceptual style embedding) and report it alongside the existing metrics, or justify why the current metrics are sufficient for the style-preservation claim.","section":"Section 6.2, 'Quantitative Evaluation using existing metrics'"}],"minor_comments":[{"comment":"The sentence 'they ignores the styles of the input sketch' has a subject-verb agreement error; it should be 'they ignore'.","section":"Section 1"},{"comment":"The sentence 'use off-the-shelf LLMs without finetuning but is limited to simple concepts' has a number mismatch; the subject is plural, so it should be 'are limited'.","section":"Section 2.3"},{"comment":"The set of sampled points is written as 'x' in the summation notation Σ_{x_k∈x}, but set x is never defined. Please define x = {x_k} explicitly after introducing the sampling procedure.","section":"Equation (2) and surrounding text"},{"comment":"The differentiable rasterizer R is not specified. Please cite the implementation (e.g., DiffVG) or provide a reference, as this is essential for reproducibility.","section":"Section 6.1"},{"comment":"The row 'Our + Qwen3' is an ablation, but it is placed in the main comparison table. Consider moving it to a separate row with a clear caption that distinguishes baselines from VLM-ablation variants.","section":"Table 1"},{"comment":"The hand-written text in these figures contains apparent typos ('cha/t_ting' and 'clu/t_tered'). Please correct these in the final figures.","section":"Figures 1 and 9"}],"recommendation":"major_revision","confidential_remarks":"The stress-test concern about Stage 2 not being validated in isolation is only partially addressed by the existing first-stage vs. full-method ablation. I agree with the skeptic that a dedicated Stage 2 reliability evaluation is the key missing piece; the current user study and aggregate metrics over 10 sketches cannot rule out frequent failures of the VLM-generated adjustment code. The paper's own acknowledgement of VLM instability makes this gap particularly important. The fit for ACM TOG is appropriate if the evaluation is strengthened."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a genuine contribution and a well-built system, but the evidence for the central claim is thinner than the presentation suggests. The new idea is to have a VLM read the partial sketch, produce a style description that augments the prompt, and later generate executable Python code that adjusts the completed strokes' SVG attributes. That two-stage use of a VLM—first for prompt augmentation, then for code-based stroke adjustment—is new relative to SketchDreamer, DiffSketcher, CLIPasso, and the LLM/SVG editing papers. The design decisions are sensible: the overlap penalty fixes a real failure mode in the baselines, and code generation avoids the token limits and hallucination that plague direct VLM SVG editing. The ablations are directionally right: adaptive augmentation beats fixed augmentation, and the full method beats first-stage-only.\n\nSoft spots. The evaluation is built on ten sketches. DreamSim/DINO/VQA means are reported without error bars or significance tests, and the user study, while strongly favoring AutoSketch, is also small and unpowered. The style-adjustment stage is the part that makes the headline claim true, and it is the least characterized: the paper admits the VLM \"occasionally overlooks some differences\" and is \"inherently unstable,\" and the only failure analysis is Figure 15. Table 1 does compare first-stage-only to the full method, which supports Stage 2's contribution overall, but it does not quantify how often the VLM's adjustment code correctly detects and fixes the right style attributes, or whether failures tend to delete strokes. No code or data are released, and the method sits on GPT-4o, ControlNet, and several unreported hyperparameters, so the results are not independently reproducible as published. These are addressable weaknesses, not fatal ones; they just make the claim \"style-consistent completion\" more of a demonstration than a measurement.\n\nCitation pattern looks fair; the related work is current and the novelty claim is reasonable. The limitations section is honest about broken guidance images, non-realtime interaction, and unsupported texture styles.\n\nWho it is for: people working on sketch generation, vector graphics editing, and VLM-guided graphics. I'd bring it to reading group. It deserves a serious referee; I would send it out, with a request for a larger evaluation set, significance tests, and ideally code/data release.","headline":"A genuinely new two-stage VLM-assisted sketch completion system with sensible design choices, but the evidence for the central claim rests on a ten-sketch evaluation with no error bars and an unquantified VLM code-generation stage.","tokens_in":15178,"tokens_out":2311,"would_cite":false,"duration_ms":25048,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"AutoSketch claims that a vision-language model can preserve a partial sketch's style by turning style descriptions into prompt augmentation and executable adjustment code, enabling two-stage vector sketch completion.","keywords":["vector sketch completion","style-aware","vision-language model","SVG editing","Bézier curves","scene completion","diffusion prior","stroke optimization"],"falsifier":"Run AutoSketch with a fixed input sketch and prompt, but substitute a deliberately generic style phrase such as 'a sketch' for the VLM's style description; if the output's style similarity to the input, measured by DreamSim, does not drop measurably relative to the full pipeline, then the central claim that VLM style descriptions drive style preservation is falsified.","tokens_in":14195,"feed_emoji":"✏️","tokens_out":6255,"duration_ms":54637,"temperature":0.7,"pith_summary":"The paper introduces AutoSketch, a method that takes a partial vector sketch and a text prompt and produces a completed sketch whose new strokes match the style of the original lines. The central proposal is that a vision-language model (VLM) can read the partial sketch, write a natural-language description of its style, and later generate executable code that adjusts the newly drawn strokes to fit that style. The paper argues that this two-stage approach lets a diffusion-based sketch generator preserve both content and style across diverse drawing styles, and it evaluates the claim with comparisons, ablations, and user studies.","feed_headline":"AutoSketch keeps a partial sketch's style while adding new strokes","feed_subtitle":"A VLM reads the input sketch's style, then emits adjustment code so new strokes match the original line look.","key_machinery":"The load-bearing mechanism is the pairing of two VLM outputs: style-description text appended to the prompt, which steers ControlNet's guidance image away from photorealism and toward the input's abstraction level, and an executable style-adjustment code snippet, which the VLM completes by translating detected differences in thickness, opacity, smoothness, curvature, and abstraction into SVG attribute changes. The intermediate sketch is represented as cubic Bézier strokes, each with control points, opacity, and width, optimized against a CLIP and LPIPS loss with an overlap penalty, then modified by the adjustment code.","core_discovery":"AutoSketch claims that style-aware sketch completion can be achieved by using a VLM twice: first, to augment the user's text prompt with style descriptions of the partial sketch so that a conditional diffusion model (ControlNet) produces a non-photorealistic guidance image; second, to detect style differences between the new strokes and the input sketch and to emit style adjustment code that fixes those differences. The key claim is that these VLM-produced style descriptions are what preserve the original stroke style, and that generating adjustment code rather than editing the SVG directly avoids content loss. The paper reports that the resulting completed sketches are preferred over SDS-based baselines and a commercial co-drawing tool in user evaluations.","pith_inferences":["A testable consequence of the paper's account is that the method's fidelity should degrade smoothly as the VLM's style description is made more generic or noisy; a controlled experiment that perturbs the style description and measures style-similarity drift would isolate how much of the style preservation actually flows through the VLM text.","The same two-stage pattern might transfer to other vector-graphics editing tasks beyond sketch completion, such as stylizing raster-turned-SVG icons or aligning generated vector art with a reference drawing, since the VLM's 'difference plus code' loop is domain-agnostic.","Because the paper reports that the VLM occasionally identifies incomplete style differences, a natural extension is to feed the detected differences back into the optimization loss of stage one rather than only using them as post-hoc SVG edits."],"forward_implications":["If AutoSketch works as described, users can start from a small partial sketch and a text prompt and get a scene-level drawing that keeps the original line style, which existing from-scratch sketch generators do not offer.","The method supports iterative completion: a user can keep strokes from a completed sketch, add new partial strokes or edit the prompt, and AutoSketch extends the drawing again without restyling the retained parts.","Because the style target is expressed in natural language and adjustment code, the approach is not tied to a fixed style parameterization, so it can handle both continuous changes such as width and opacity and discrete edits such as stroke deletion.","The paper's ablation results indicate that both the adaptive prompt augmentation and the code-based adjustment are needed: fixed text augmentation leaves blurred guidance, and direct VLM SVG editing drops strokes."],"supporting_citations":[{"why":"Supplies the ControlNet conditional diffusion model that generates guidance images from the augmented prompt.","marker":"[Zhang et al. 2023]"},{"why":"Provides the stroke-optimization objective and parameterization that Stage 1 adapts, and serves as the main SDS-based baseline.","marker":"[Xing et al. 2023]"},{"why":"Contributes SketchDreamer, a baseline and source of the ControlNet scribble conditioning used for comparison.","marker":"[Qu et al. 2023]"},{"why":"Supplies CLIPasso-generated input sketches whose varied styles the method must preserve.","marker":"[Vinker et al. 2022]"},{"why":"Identifies the GPT-4o model used as the VLM for style descriptions and adjustment-code generation.","marker":"[Hurst et al. 2024]"},{"why":"The OpenSketch dataset provides professional design-sketch inputs used in the experiments.","marker":"[Gryaditskaya et al. 2019]"}],"fun_headline_variants":["AutoSketch: VLM-guided sketch completion that keeps style","AutoSketch: VLM reads style, then completes the sketch","Sketch completion that preserves style via VLM description","AutoSketch: style-aware completion via VLM style notes"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes that a vision-language model can describe the style of a rasterized partial sketch accurately enough that the words steer ControlNet appropriately and give the adjustment code a clear target, so if the description is imprecise, generic, or verbose, the completed sketch will drift away from the input style.","fun_headline_variants_meta":{"raw":{"variants":["AutoSketch: VLM-guided sketch completion that keeps style","AutoSketch: VLM reads style, then completes the sketch","Sketch completion that preserves style via VLM description","AutoSketch: style-aware completion via VLM style notes"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00064,"raw_usage":{"total_tokens":2921,"prompt_tokens":892,"completion_tokens":2029,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":508,"completion_tokens_details":{"reasoning_tokens":1959}},"tokens_in":508,"tokens_out":2029,"duration_ms":15255,"temperature":1.0,"reasoning_tokens":1959,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T19:32:49.508240+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run AutoSketch with a fixed input sketch and prompt, but substitute a deliberately generic style phrase such as 'a sketch' for the VLM's style description; if the output's style similarity to the input, measured by DreamSim, does not drop measurably relative to the full pipeline, then the central claim that VLM style descriptions drive style preservation is falsified.","supporting_citations":[],"review_version":1}