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REVIEW 4 major objections 5 minor 51 references

InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Freezing spatial layers of a video diffusion model yields sharper, consistent step-by-step picture guides from text alone.

desk verdict Practical, well-ablated method for visual instruction generation, but the 'best overall' claim rides on a post hoc metric weighting and does not survive alpha=1. read the letter →

arxiv 2608.08460 v1 pith:ZHXZUHZL submitted 2026-08-09 cs.CV

Shun Okamoto , Satoshi Iizuka , Kazuhiro Fukui This is my paper · ORCID
classification cs.CV
keywords visualinstructiongenerationvideodiffusionmodelsspatial-freezetrainingconsistentadaptercontext-awaretemporalstepfaithfulnesscross-imageconsistencyparameter-efficientfinetuning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

InstructionCrafter claims that the best way to generate a coherent sequence of step-by-step pictures from text is to keep the spatial layers of a pretrained video diffusion model frozen and train only the temporal and text-conditioning pathways. The paper argues that step faithfulness, cross-image consistency, and per-frame visual quality need not be a fixed trade-off when the model's spatial prior is protected from low-quality training data. Two lightweight adapters then inject instructional context into text and temporal attention, so the model can learn multi-step relations without absorbing dataset artifacts. This matters because it offers a practical text-only route to automatic visual manuals, which could help anyone following procedural instructions without needing reference images.

What carries the argument

The load-bearing mechanisms are spatial-freeze training plus two instruction-aware adapters. Spatial-freeze training keeps the frozen spatial convolutions and spatial self-attention layers that control local texture and high-frequency detail, so the pretrained image-quality prior stays intact while temporal and cross-attention layers adapt to procedural semantics. The Consistent Adapter is a stack of four transformer layers with global attention over all step tokens and adjacent attention over neighboring steps, capturing both task-level dependencies and local transitions. The Context-Aware Temporal Adapter forms a Gram matrix $G_s = C_s C_s^\top$ from a spatial slice of cross-attention outputs and adds a per-head learnable bias $B_s = \beta G_s$ to temporal self-attention, so frames with similar step semantics attend to each other; both adapters are zero-initialized to behave as identity at the start of training.

What would settle it

Re-run the full evaluation with SC-F computed at $\alpha=1$ or with a pre-registered user study that weights step faithfulness and cross-image consistency equally; the paper's supplementary Table 1 shows that under $\alpha=1$ Stable Diffusion, not InstructionCrafter, achieves the best SC-F on ShowHowTo.

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Extended reading notes

Core claim

The central discovery is that separating the optimization of temporal and instructional alignment from per-frame visual quality lets a single model achieve state-of-the-art overall performance on both ShowHowTo and WikiHow-VGSI. Building on VideoCrafter2, the authors freeze all spatial convolutional blocks and spatial self-attention layers, finetune temporal attention and cross-attention on per-step texts, and add the Consistent Adapter and Context-Aware Temporal Adapter. The Consistent Adapter aggregates textual cues from the whole instruction sequence and from neighboring steps so object identity and attributes persist across frames, while the Context-Aware Temporal Adapter converts cross-attention outputs into a step-conditioned bias for temporal self-attention, propagating inter-step relations. This combination reduces trainable parameters from 1.4B to 0.7B while improving VIEScore perceptual quality from 0.662 to 0.742 and achieving the best SC-F, Semantic, and overall balance of scores on both benchmarks.

Load-bearing premise

The state-of-the-art claim depends on the chosen SC-F weighting, where a neutral choice ($\alpha=1$) changes the ShowHowTo ranking and would put Stable Diffusion ahead.

Editorial extensions

If this is right

  • Text-only visual instruction generation without any reference image can outperform pipelines that tile frames, generate autoregressively, or finetune the full backbone.
  • Freezing spatial layers reduces trainable parameters by about half and suppresses artifacts such as noise, blur, watermarks, and spurious subtitles learned from low-quality web video.
  • The two adapters work best together: ablations show that either adapter alone underperforms the full combination on both semantic and perceptual scores.
  • Diffusion Transformer backbones with 3D VAE temporal compression are less suited to per-step text conditioning, since their compressed latents make step-specific semantics harder to preserve.
  • The approach generalizes across diverse procedural domains including cooking, DIY, electronics repair, and crafts, as shown on the WikiHow-VGSI zero-shot evaluation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The SC-F meta-metric's weighting is a free parameter, and the paper's own supplementary results show that with $\alpha=1$, Stable Diffusion leads SC-F on ShowHowTo; a different principled weighting could therefore change the headline ranking.
  • The Gram-matrix bias mechanism is generic enough to be reused in other multi-condition sequence generation tasks such as storyboards, comic panels, or medical procedure illustrations, where per-frame text conditions vary while visual identity must persist.
  • A user study that weights step faithfulness more heavily than cross-image consistency might shift preferences toward zero-shot methods, whose main weakness in the current protocol is low consistency rather than low visual quality.
  • The spatial-freeze recipe could be tested as a general recipe for adapting video diffusion models to any task with per-frame text conditioning, not just visual instructions, whenever the training data is visually noisier than the pretraining data.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes InstructionCrafter, a diffusion-based framework for generating step-by-step visual instructions from textual procedure descriptions. The key ideas are (i) spatial-freeze training, which freezes spatial layers of a pretrained video diffusion backbone (VideoCrafter2) while finetuning temporal, cross-attention, and text-conditioning pathways, and (ii) two lightweight adapters: a Consistent Adapter that aggregates global and adjacent step-text context, and a Context-Aware Temporal Adapter that injects step-conditioned biases into temporal self-attention. The method is evaluated on ShowHowTo and WikiHow-VGSI using CLIP-based Step Faithfulness (SF), DINOv3-based Cross-Image Consistency (CIC), a weighted SF-CIC F-score (SC-F), and VLM-based VIEScore semantic/perceptual components. The paper claims state-of-the-art overall performance on both datasets and includes ablations on training strategy, adapter design, and cross-attention finetuning, plus a small user study.

Significance. If the claimed results hold, the paper offers a practical text-only pipeline for generating coherent, faithful, and artifact-free visual instructions, and the spatial-freeze idea is a plausible way to preserve pretrained per-frame quality while adapting temporal and text pathways. The ablations are informative and the adapters are lightweight and zero-initialized. However, the central state-of-the-art claim is not robustly supported: the SC-F metric weighting (alpha=2) is selected after observing the empirical score ranges, and with alpha=1 the paper's own supplementary results show Stable Diffusion ahead on ShowHowTo. In addition, the method does not dominate on the component faithfulness metrics on ShowHowTo, and the most directly competing prior method (Souček et al./ShowHowTo) is not included in the quantitative comparison. These issues collectively mean the paper's headline contribution needs revision rather than acceptance as-is.

major comments (4)
  1. [Section 4.1, SC-F definition, and Supplementary Table 1 (B.2)] The SC-F composite metric uses alpha=2, justified as balancing the empirical ranges of SF and CIC observed in the authors' own experiments. Supplementary Table 1 shows that with alpha=1, Stable Diffusion achieves the best SC-F on ShowHowTo (0.468 vs. 0.435 for InstructionCrafter). Since the headline claim in Section 4.2 that InstructionCrafter 'achieves the best overall performance on both datasets' rests on this weighting, the ranking is not robust to the metric's free parameter. The authors should either justify alpha independently of the observed results, report the full alpha-sweep prominently as the primary result, or temper the claim to a weighted-aggregate statement.
  2. [Section 4.2, Table 1] Even under the chosen alpha=2, the claimed 'best overall' is not a domination: on ShowHowTo, the Baseline has higher SF (0.464 vs. 0.455) and higher Semantic score (0.555 vs. 0.537), while InstructionCrafter wins only on CIC and Perceptual. The SC-F gap over the Baseline is also small (0.460 vs. 0.455). The manuscript should explicitly state this trade-off structure and avoid implying that the method is uniformly best on faithfulness, consistency, and quality; the strengths are consistency and perceptual quality at a small cost in step-faithfulness on ShowHowTo.
  3. [Section 4.1, Compared Methods, and Section 4.2] The paper lists Souček et al. [40] (ShowHowTo) as a compared method category but does not include it in Table 1; instead, it introduces a self-defined Baseline (fully finetuned VideoCrafter2 with per-frame text conditioning). Since Souček et al. use an image-to-video model with first-frame reference, the absence of this directly competing prior method or a faithful adaptation of it leaves the claim of outperforming the actual state-of-the-art visual-instruction method unsubstantiated. At minimum, the authors should either incorporate the original ShowHowTo method (with its reference-image setting) or clearly argue why the setting difference makes it non-comparable, and avoid saying the method outperforms [40] on the basis of Table 1.
  4. [Section 4.5, Table 7] The user study involves only 15 participants, reports no confidence intervals or significance tests, and the average preference over the Baseline is 53%. This is too weak to independently validate the alpha=2 weighting or the claim of overall superiority in human preference. The authors should add statistical testing (e.g., binomial tests or confidence intervals per baseline) or present the study as a pilot with corresponding caveats.
minor comments (5)
  1. [Section 3.2, Section 1, Table 4] The trainable parameter counts are inconsistent: Section 3.2 says '1.4B vs 0.7B', the Introduction says 'from 1.4B to 0.6B', and Table 4 lists 0.6B for Spatial-Freeze and 0.7B for the full model. The authors should unify these numbers and clarify whether 0.6B refers to a specific ablation or a typo.
  2. [Section 4.3, Open-Sora comparison] The modifications from Open-Sora to Open-Sora++ are described only as 'several efforts' and 'adjusting several hyperparameters'; for reproducibility, the authors should list the exact architectural changes and hyperparameter settings.
  3. [Supplementary Section B.2] The statement that improvements 'may seem marginal' in the supplementary is rather important and should be reflected in the main text's discussion of effect sizes and practical significance, rather than only appearing in the appendix.
  4. [Table 1 caption] The caption mentions red/blue marking for best/second-best values, which is conventional, but the actual table formatting in the PDF must be checked so that the colors are distinguishable in grayscale printing; adding bold or symbols would improve clarity.
  5. [Section 4.1, CIC metric] The CIC metric uses DINOv3 features but the reference [36] is cited as 'Siméoni et al.' with a different spelling in the reference list; please ensure the spelling and citation are consistent.

Circularity Check

1 steps flagged · score 6.0 of 10

The 'best overall performance' claim is partly forced by the post hoc choice of the SC-F metric weight alpha=2; under alpha=1, Stable Diffusion wins on ShowHowTo.

  1. fitted input called prediction [Section 4.1 (Evaluation Metrics, SC-F definition), Section 4.2 (Quantitative Results), Supplementary B.2 (Full Quantitative Results, Table 1)]
    "where we set α=2 to place greater weight on consistency to balance the empirical ranges of SF (typically 0.20-0.60) and CIC (typically 0.40-0.55) observed in our experiments. ... Stable Diffusion achieves the best SC-F with α=1 where vanilla F-score is applied, but this is counterintuitive since it has the lowest CIC. In contrast, SC-F with larger α values better reflects the overall performance, where our method outperforms others. Therefore, the selected α=2 is reasonable."

    The headline claim 'InstructionCrafter achieves the best overall performance on both datasets' (Section 4.2) is evaluated with the SC-F metric whose free parameter alpha is set to 2 after observing the empirical SF/CIC ranges and after observing that alpha=1 gives a different winner. Supplementary Table 1 shows Stable Diffusion beats InstructionCrafter on ShowHowTo under alpha=1 (0.468 vs 0.435), while alpha=2 reverses the ranking (0.460 vs 0.426). On ShowHowTo, InstructionCrafter also trails the Baseline on the VLM Semantic component (0.537 vs 0.555), so its 'overall' advantage comes from the CIC component amplified by the chosen alpha.

full rationale

The paper's core technical derivation — spatial-freeze training plus the Consistent Adapter and Context-Aware Temporal Adapter — is self-contained and is not circular: the adapters are defined operationally, the frozen-spatial-layer strategy is an architectural choice tested by ablations, and the component metrics (CLIP-based SF, DINOv3-based CIC, VLM-based VIEScore) are externally defined and do not embed the method's output. There is no load-bearing self-citation chain and no renamed known result. The circularity is localized to the headline 'overall SOTA' claim: SC-F with alpha=2 is chosen in Section 4.1 using the authors' own observed score ranges, and the supplementary explicitly concedes that alpha=1 gives the best SC-F to Stable Diffusion on ShowHowTo. Because the paper's own evidence shows the ranking flips with the metric's free parameter, the central 'best overall on both datasets' statement reduces in part to that post hoc weighting choice rather than to a parameter-free property of the model. This warrants a moderate circularity score of 6.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests primarily on the evaluation protocol and on unverified domain assumptions about where artifacts live in the network. The only hand-fitted number that directly changes the ranking is the SC-F alpha weighting. No new physical or conceptual entities are introduced.

free parameters (1)
  • SC-F alpha weighting = 2 (selected from observed SF 0.20-0.60 and CIC 0.40-0.55 ranges)
    The composite metric's weighting is hand-chosen after inspecting the authors' own metric ranges and the supplementary shows alpha=1 would rank Stable Diffusion first on ShowHowTo, so the SOTA claim depends on this value.
assumptions (5)
  • domain assumption Freezing spatial layers preserves the pretrained per-frame quality prior and prevents absorption of dataset-specific artifacts.
    Section 3.2 'How it works' asserts this mechanism; the ablation shows improved perceptual scores, but the paper does not directly measure where artifacts are encoded.
  • domain assumption DINOv3 feature distances quantify cross-image consistency and the VIEScore protocol with Qwen3-VL quantifies semantic and perceptual quality.
    Section 4.1 defines CIC via DINOv3 pairwise closeness and uses Qwen3-VL as judge; no calibration against human ratings is provided.
  • domain assumption Artifacts in fully finetuned models originate from the low quality of automatically extracted ShowHowTo training data.
    Section 1 and 3.2 attribute noise, blur, and subtitles to web-sourced training videos; this motivates spatial-freeze but is not independently verified.
  • domain assumption VideoCrafter2 is a suitable backbone because its lack of temporal compression preserves per-frame latent representations for step-wise conditioning.
    Section 3.1 motivates the backbone choice; comparisons with Open-Sora (which uses 3D VAEs) support it, but only for the tested backbones.
  • standard math Standard diffusion denoising objective and classifier-free guidance remain valid for this task.
    The method trains with the noise-prediction loss (supplementary Eq. 2) and CFG (Eq. 3), relying on established diffusion theory.

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Cite this review

Pith. "Pith review of InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions." pith.science (2026). https://pith.science/paper/ZHXZUHZL

@misc{pith2026260808460,
  author       = {Pith},
  title        = {Pith review of: InstructionCrafter: Generating Consistent and High-Fidelity Visual Instructions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHXZUHZL}},
  note         = {Machine review of arXiv:2608.08460}
}
read the original abstract

Given textual task instructions, generating step-by-step visual instructions as an image sequence requires the simultaneous satisfaction of multiple properties, specifically step faithfulness, cross-image consistency, and per-frame visual quality. Existing text-to-image generation approaches rarely meet all three properties, owing to independent sampling that breaks consistency, finetuning on low-quality video that degrades per-frame quality, and frozen backbones that lack multi-step understanding. In this work, we propose InstructionCrafter, a diffusion-based framework with the key idea of separating the optimization of temporal and instructional alignment from per-frame visual quality via (1) spatial-freeze training and (2) instruction-aware adapters. Built on a pretrained video diffusion backbone, InstructionCrafter freezes the spatial layers that control per-frame detail and updates only temporal and text-conditioning pathways to learn instruction semantics and inter-step relations, which preserves the generative prior for per-frame quality and reduces trainable parameters by about 50 percent compared with full finetuning. We also introduce two lightweight adapters that enhance the model's understanding of instructional context. The Consistent Adapter aggregates textual cues from the entire instruction sequence and from neighboring steps to keep object identity and attributes consistent across frames, and the Context-Aware Temporal Adapter converts cross-attention outputs into biases for temporal self-attention, explicitly propagating inter-frame relations. Extensive experiments on two benchmark datasets demonstrate state-of-the-art overall performance on step faithfulness, cross-image consistency, and per-frame visual quality while significantly reducing noise, blur, and spurious subtitles. Our code and trained models will be publicly available.

Figures

Figures reproduced from arXiv: 2608.08460 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Limitations of existing approaches. Zero-shot Text-to-Image (T2I) diffusion models [33] often fail to maintain cross-image consistency. Instruction-oriented T2I exten￾sions [5], referred to as Enhanced T2I, improve consistency yet lack temporal priors, reduc￾ing step faithfulness. Text-to-Video (T2V) models [8] fully finetuned on visual instruction datasets [40] produce more faithful and consistent sequences, but pe… view at source ↗
Figure 3
Figure 3. Overview of InstructionCrafter. We build upon a latent video diffusion back￾bone [8] and introduce two novel lightweight adapters: Consistent Adapter and Context￾Aware Temporal Adapter. Consistent Adapter captures inter-step relations among step texts via global and adjacent attention mechanisms. Context-Aware Temporal Adapter incor￾porates step text context into temporal attention via attention biases derived from … view at source ↗
Figures from the paper (30 more)
Figure 4
Figure 4. Figure 4: Comparison of different training strategies. Red boxes highlight artifacts and inconsistencies. Full Finetuning learns technical artifacts from low-quality training data. Zero-Shot generation, which directly uses the pretrained video diffusion model with per￾frame text…
Figure 5
Figure 5. Figure 5: Qualitative comparison. Red boxes highlight inconsistencies and unfaithful gen￾erations. Blue dotted boxes highlight technical artifacts such as blurriness and watermarks. Existing methods fail to generate high-quality, consistent, and step-faithful images, while our I…
Figure 6
Figure 6. Figure 6: Diverse examples generated by InstructionCrafter. InstructionCrafter can han￾dle various tasks and sequence lengths, generating high-quality visual instructions that are consistent across steps and faithful to the instructions [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 1
Figure 1. Figure 1: Correlation between InstructionCrafter’s win rates and automatic metric scores of compared methods. Each point rep￾resents a baseline method, with its x-coordinate indicating the InstructionCrafter’s win rate against that method in the user study, and its y-coordinate …
Figure 2
Figure 2. Figure 2: Examples of VIEScore evaluation. The Vision Language Model is able to follow the prompts and provide reasonable scores along with rationales. Input images are generated by our method. Step 1 Step 2 Step 3 Step 4 Step 5 The images show hands interacting with engine comp…
Figure 3
Figure 3. Figure 3: Examples of VIEScore evaluation on low quality images. The Vision Language Model is able to follow the prompts and provide reasonable scores along with rationales even for low-quality images. Input images are generated by full finetuning baseline. 6 [PITH_FULL_IMAGE:f…
Figure 4
Figure 4. Figure 4: Qualitative comparison between finetuning with fewer steps and our method. Red boxes indicate significant degradation in visual quality. Full finetuning baselines still cause significant degradation in visual quality with or without fewer finetuning steps, while our me…
Figure 5
Figure 5. Figure 5: Qualitative comparison between InstructionCrafter and StoryDiffusion [50] and ContextualStory [48]. Red boxes highlight inconsistencies and unfaithful generations. Blue dotted boxes highlight technical artifacts such as blurriness and watermarks. StoryDiffu￾sion fails …
Figure 6
Figure 6. Figure 6: Qualitative comparison between InstructionCrafter and Open-Sora [49]. Open-Sora employs 3D VAE with temporal compression, which leads to difficulties in generating visual instructions where each step often describes different actions and scenes. Open-Sora++ is our modi…
Figure 7
Figure 7. Figure 7: Qualitative comparison between InstructionCrafter and AnimateDiff [10]. Red boxes indicate significant degradation in visual quality. Both AnimateDiff and our modified version AnimateDiff++ fail to maintain instruction consistency and generate high￾quality images, whil…
Figure 8
Figure 8. Figure 8: Additional qualitative comparison on ShowHowTo. 11 [PITH_FULL_IMAGE:figures/full_fig_p030_8.png]
Figure 9
Figure 9. Figure 9: Additional qualitative comparison on ShowHowTo. 12 [PITH_FULL_IMAGE:figures/full_fig_p031_9.png]
Figure 10
Figure 10. Figure 10: Additional qualitative comparison on ShowHowTo. 13 [PITH_FULL_IMAGE:figures/full_fig_p032_10.png]
Figure 11
Figure 11. Figure 11: Additional qualitative comparison on ShowHowTo. 14 [PITH_FULL_IMAGE:figures/full_fig_p033_11.png]
Figure 12
Figure 12. Figure 12: Additional qualitative comparison on ShowHowTo. 15 [PITH_FULL_IMAGE:figures/full_fig_p034_12.png]
Figure 13
Figure 13. Figure 13: Additional qualitative comparison on ShowHowTo. 16 [PITH_FULL_IMAGE:figures/full_fig_p035_13.png]
Figure 14
Figure 14. Figure 14: Additional qualitative comparison on ShowHowTo. 17 [PITH_FULL_IMAGE:figures/full_fig_p036_14.png]
Figure 15
Figure 15. Figure 15: Additional qualitative comparison on ShowHowTo. 18 [PITH_FULL_IMAGE:figures/full_fig_p037_15.png]
Figure 16
Figure 16. Figure 16: Additional qualitative comparison on ShowHowTo. 19 [PITH_FULL_IMAGE:figures/full_fig_p038_16.png]
Figure 17
Figure 17. Figure 17: Additional qualitative comparison on ShowHowTo. 20 [PITH_FULL_IMAGE:figures/full_fig_p039_17.png]
Figure 18
Figure 18. Figure 18: Additional qualitative comparison on WikiHow￾VGSI. Add a little cornstarch. Knead the food coloring and cornstarch into the chocolate. Stable Diffusion StackedDiffusion Bordalo et al. Baseline InstructionCrafter [PITH_FULL_IMAGE:figures/full_fig_p040_18.png]
Figure 20
Figure 20. Figure 20: Additional qualitative comparison on WikiHow-VGSI. 22 [PITH_FULL_IMAGE:figures/full_fig_p041_20.png]
Figure 21
Figure 21. Figure 21: Additional qualitative comparison on WikiHow￾VGSI. Wash the chicken legs. Coat the chicken in the butter mixture. Serve the chicken. Stable Diffusion StackedDiffusion Bordalo et al. Baseline InstructionCrafter [PITH_FULL_IMAGE:figures/full_fig_p042_21.png]
Figure 23
Figure 23. Figure 23: Additional qualitative comparison on WikiHow-VGSI. 24 [PITH_FULL_IMAGE:figures/full_fig_p043_23.png]
Figure 24
Figure 24. Figure 24: Additional qualitative comparison on WikiHow-VGSI. 25 [PITH_FULL_IMAGE:figures/full_fig_p044_24.png]
Figure 26
Figure 26. Figure 26: Additional qualitative comparison on WikiHow￾VGSI. 26 [PITH_FULL_IMAGE:figures/full_fig_p045_26.png]
Figure 27
Figure 27. Figure 27: Additional qualitative comparison on WikiHow-VGSI. 27 [PITH_FULL_IMAGE:figures/full_fig_p046_27.png]
Figure 28
Figure 28. Figure 28: Additional qualitative examples of our method on ShowHowTo [36] and WikiHow-VGSI [45]. Our InstructionCrafter generates diverse, high-quality, consistent, and step-faithful images across various instructional scenarios. 28 [PITH_FULL_IMAGE:figures/full_fig_p047_28.png]
Figure 29
Figure 29. Figure 29: Failure cases where none of the methods could generate satisfactory images. 29 [PITH_FULL_IMAGE:figures/full_fig_p048_29.png]
Figure 30
Figure 30. Figure 30: Failure cases where none of the methods could generate satisfactory images. 30 [PITH_FULL_IMAGE:figures/full_fig_p049_30.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.