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REVIEW 4 major objections 5 minor 12 references

This paper claims that a pretrained text-to-video diffusion model can be fine-tuned with just seven hand-drawn sketches to generate sequential, order-controlled sketching videos, using LLMs for planning and the video model for rendering.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-02 22:42 UTC pith:I4M6NXTD

load-bearing objection A genuinely clever two-stage fine-tuning recipe gets video diffusion models to draw stroke-by-step sketches from text, but the ordering-fidelity evidence is thinner than the claims — worth a serious referee, with revisions. the 4 major comments →

arxiv 2602.15819 v2 pith:I4M6NXTD submitted 2026-02-17 cs.CV

VideoSketcher: Sequential Sketch Generation Using Video Model Priors

Hui Ren , Yuval Alaluf , Omer Bar Tal , Alexander Schwing , Antonio Torralba , Yael Vinker This is my paper
classification cs.CV
keywords sequential sketch generationvideo diffusion modelsstroke orderingfew-shot fine-tuningtext-to-video generationbrush style controlautoregressive generationsketch-as-video representation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper claims that a pretrained text-to-video diffusion model can be turned into a sequential sketch generator with almost no human sketch data: seven hand-drawn examples, arranged in a two-stage fine-tuning scheme, are enough to produce videos of strokes being drawn one by one in a text-specified order. The authors argue that LLMs and video models have complementary strengths — LLMs decide what to draw and in what order, video models render the strokes with visual quality — and that the temporal structure of drawing can be learned from synthetic shapes before being transferred to real sketch style. If right, this removes the need for large-scale human drawing datasets and opens a route to interactive co-drawing in pixel space.

Core claim

The central discovery is that a text-to-video diffusion model can be distilled into a sketching model by decoupling two kinds of knowledge: stroke ordering and visual appearance. Ordering is taught first with synthetic compositions of simple geometric shapes arranged in Gestalt-like relationships and rendered in several orders; appearance is then transferred from just seven real sketches. The resulting model generates a short video in which black strokes progressively accumulate on a blank canvas, and it tracks the numbered drawing steps supplied in the text prompt. The authors also show the same framework extends to brush-style control from a visual cue and to autoregressive generation, whi

What carries the argument

The paper's central object is a sketch sequence rendered as a short pixel-space video from SVG paths, where each frame introduces at most one new stroke. This representation lets the authors fine-tune a pretrained video diffusion model with a standard rectified-flow loss; a two-stage schedule separates learning drawing grammar (synthetic shape compositions) from learning appearance (seven hand-drawn sketches). The SVG parametric source also enables brush-style variation without extra human effort.

Load-bearing premise

The load-bearing premise is that the fine-tuned video model will keep introducing strokes one at a time as taught by the training videos, even though the paper's own measurements show that roughly 20% of frames on simple concepts and 37% on scenes contain multiple new strokes.

What would settle it

Measure the frequency of frames containing multiple disjoint strokes, as the paper does in its supplementary evaluation. If that frequency approaches or exceeds 50% for the simple QuickDraw concepts under the standard 81-frame protocol—rather than the reported ~20%—then the temporal constraint that carries the method would be broken, and the 'stroke-by-stroke' description would become inaccurate.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Sequential sketch generation no longer requires millions of human stroke sequences; a handful of examples suffices when starting from a video model prior.
  • Text-specified drawing order can control which semantic part is drawn when, enabling different drawing trajectories for the same concept.
  • Brush style and color can be transferred from a visual exemplar without explicit parametric stroke representations.
  • The distilled model can generate synthetic training data for an autoregressive video model, enabling real-time co-drawing.
  • The approach inherits both the strengths and limitations of the base video model: it excels at visually rich concepts but struggles with symbolic or mathematical content.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the two-stage decoupling is the real driver, the same recipe should transfer to other structured temporal processes—handwriting, diagram construction, or progressive painting—using the same handful-of-examples fine-tuning, making the method a general prior for process generation.
  • The paper's own measurement of roughly 20% multi-stroke frames on simple concepts suggests an upper bound on how strictly 'one stroke at a time' is satisfied; a stricter temporal prior (e.g., explicit stroke-ordering tokens or longer videos) may be needed as concepts become more complex.
  • Because the training renderer is SVG-based, the same pipeline could be paired with a differentiable rasterizer to recover parametric strokes from the generated pixels, combining diffusion's visual quality with editability.
  • The LLM planner could be replaced or augmented with learned planners, since the renderer is now order-controllable; the bottleneck for ordering fidelity may shift from the LLM's semantic decomposition to the video model's temporal adherence.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper introduces VideoSketcher, a method for generating sequential sketches as videos by fine-tuning a pretrained text-to-video diffusion model (Wan 2.1). A two-stage procedure first teaches stroke ordering on synthetic shape compositions, then transfers sketch appearance from seven hand-drawn examples. An LLM is used at inference to decompose a text prompt into a numbered drawing order. The paper also presents brush-style conditioning via first-frame exemplars and an autoregressive variant based on CausVid trained partly on videos generated by the diffusion model. The central claim is that, despite minimal real sketch data, the method produces high-quality sketch sequences that closely follow text-specified drawing orders.

Significance. If the claims hold, the contribution is valuable: it demonstrates a data-efficient route to pixel-space sequential sketch generation by repurposing video diffusion priors, avoiding large stroke-sequence datasets and parametric stroke representations. The two-stage decoupling of ordering and appearance is a practical contribution, and the brush-conditioning and co-drawing extensions broaden the applicability. The paper is transparent about implementation details (LoRA rank, epochs, inference costs) and about known limitations, and the supplementary material is extensive. However, the quantitative support for the main claim is incomplete: the final-frame recognition metric does not show an improvement over the base video model, and the ordering-fidelity metric relies on an unvalidated LLM preference test; the reported multi-stroke rates directly weaken the stroke-by-stroke ordering premise. The ablation in Table 2 does support the two-stage design over single-stage alternatives, which is a genuine strength.

major comments (4)
  1. [§5.1, Table 1] The central claim includes 'high-quality' sequential sketches, but the only quantitative metric on the main task—CLIP recognition of the final frame—gives Ours 0.82 Top-1, below Naive Wan2.1 (0.92) and PaintsUndo (1.00). The text dismisses Wan2.1 because it produces near-identical frames and PaintsUndo because it conditions on a final image, but no metric combines final-frame quality with temporal progression. As reported, Table 1 cannot distinguish 'better temporal behavior at some quality cost' from 'worse final quality.' A quantitative comparison that controls for temporal collapse, or a recognition metric evaluated only on the final frame with an explicit temporal-consistency guarantee, is needed to support the high-quality part of the claim.
  2. [§5.5 and Supplementary Section B (Quantitative Metrics)] Ordering fidelity is measured only by an LLM preference test, which is not validated against human judgments. The LLM is used at inference to generate the target order (Section 4.2), and a similar LLM is used at evaluation to extract the order from a generated video and compare it with the target. The protocol supplies the target ordering vocabulary to the evaluator, which can mechanically inflate agreement. Since 'closely follow text-specified orderings' is the paper's central claim, an absolute measure of how often the generated part order matches the prompt order, or a human evaluation of the preference comparisons, is required. The current protocol establishes only that the full model is preferred over two ablations, not that it faithfully follows the prompt.
  3. [Supplementary B.4, Table 3; §4.1] Section 4.1 defines the training representation as ensuring 'at most one stroke introduced per frame' and Figure 2 emphasizes 'clean temporal structure.' Supplementary Table 3 reports that 19.58% of QuickDraw frames and 36.96% of scene-level frames contain multiple disjoint strokes. This is a direct violation of the stroke-level premise on which the method's sketch-by-sketch behavior rests, and the rate is substantial for complex scenes. The paper acknowledges this in Limitations, but the numbers are large enough that the claim of 'faithful' stroke-order control is materially weakened. The authors should quantify how often the semantic-part order actually matches the prompt, especially in multi-stroke frames, and clarify whether strokes that appear together belong to the same semantic part or to different parts.
  4. [§5.5, Table 2] The ablation table reports ordering fidelity only as pairwise LLM preferences. Even if the full model is preferred over the primitives-only and real-sketches-only variants, this does not establish that the full model follows the text-specified order with high fidelity in an absolute sense. In particular, the 'Primitives vs. Full' comparison gives Full 53.4% and Primitives 26.9%, leaving 19.7% 'Neither'; the full model is not preferred in roughly 46% of the comparisons. Reporting the raw rates at which each variant matches the target order would be more informative.
minor comments (5)
  1. [Table 2] The table formatting is broken: 'Primitives vs. 7-Human' row shows '– –50.037.2 12.8' without a separator, and the '– –' placeholders are confusing. Please fix the formatting.
  2. [Section 5.1] The phrase 'we follow standard practice [2023; 2022; 2025]' has malformed citations with no author names; these should be replaced with proper citation keys.
  3. [Title/authors] The author/header block contains undecipherable text ('NPEFMVTFS ʜ' and similar), which appears to be a rendering artifact and should be cleaned before publication.
  4. [Section 5.5] The text says the full model achieves CLIP recognizability 'comparable' to the model trained only on seven real sketches, but Table 2 shows 0.82 vs 0.88 Top-1; consider reporting standard deviations or using 'slightly lower but within variance.'
  5. [Supplementary Figure 21] The notation 'T2V 14B' is used without definition; please define it in the caption or main text.

Circularity Check

0 steps flagged

No circular derivation; the paper's claims are empirical and evaluated against external baselines.

full rationale

The paper contains no equation-level derivation that reduces to its own inputs. The method is an empirical fine-tuning pipeline: stage 1 trains on synthetic SVG shape compositions rendered with controlled stroke order, and stage 2 adapts appearance from seven hand-drawn sketches. Quantitative evaluation is measured against external or quasi-external references: CLIP final-frame recognition is benchmarked against human QuickDraw drawings, SketchAgent, Wan 2.1, and PaintsUndo (Table 1); sketch progression is measured with added-pixel curves compared with human data (Fig. 23); brush control is measured with Gram-matrix distances against a random-exemplar baseline. The only arguably self-referential element is the LLM-based ordering-fidelity protocol (Supplementary B; Table 2), where an LLM extracts part orderings from generated videos and compares them with target orderings, while the same class of LLM is used at inference to produce target plans (Section 4.2). However, this is a measurement-validity concern rather than a circular derivation: the generated videos are not defined by the LLM judge's output, no parameter is fitted to the judge, and the paper itself acknowledges the metric's weakness for primitive-only sketches (Section 5.5). Likewise, the multi-stroke emergence statistics (Supplementary Table 3: 19.58% for QuickDraw concepts, 36.96% for scenes) directly contradict the 'at most one new stroke per frame' property used to construct training data (Section 4.1), but the paper explicitly reports this as a limitation of pixel-space generation; it is not a step that reduces to an input. The autoregressive variant is trained partly on videos synthesized by the paper's own diffusion model, yet the paper transparently reports the resulting quality gap and does not claim the AR model matches the diffusion model; this is self-distillation, not circularity. Self-citations (e.g., following SketchAgent's evaluation protocol, invoking LLM planning) are contextual and are not load-bearing for the central result. Therefore no circular step can be quoted, and the appropriate finding is no significant circularity.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical or conceptual entities: 'drawing grammar' is a descriptive term, and the video prior is an existing model. The central assumptions are all domain assumptions about transferability of few-shot fine-tuning and LLM planning. The main free parameters are hand-chosen dataset sizes and training hyperparameters; their sensitivity is not examined.

free parameters (4)
  • Stage-1 synthetic dataset size = 15 videos, 700 epochs
    The temporal-ordering grammar is learned from only 15 manually constructed SVG composition videos; the specific compositions and their three order variants are hand-chosen and not released.
  • Stage-2 real sketch set = 7 sketches: lamp, car, chair, tree, cup, butterfly, flower
    Appearance distillation depends entirely on this hand-picked set; the paper does not analyze sensitivity to this selection.
  • LoRA rank / learning rate = rank 32, lr 1e-4, 700+700 epochs
    Training hyperparameters chosen by hand; no sweep is reported, so their role in the central result is untested.
  • Inference steps = 50 (10 also plausible)
    Denoising steps chosen for best visual quality; the reported qualitative results use 50 steps.
axioms (4)
  • domain assumption Pretrained Wan 2.1 video diffusion model provides a strong, transferable visual and temporal prior for stroke-based sketch sequences (Section 3).
    The entire method rests on the base model's capability; Supplementary Section B.1 shows that limitations of the base model (e.g., math functions) transfer directly to the fine-tuned model.
  • domain assumption Synthetic Gestalt shape compositions teach a generalizable stroke-ordering grammar that transfers to real, complex sketches (Section 4.3).
    The two-stage design assumes ordering learned from primitives transfers after only seven appearance examples; the ablation (Table 2) supports this but only via LLM-judged preference, which the paper admits can be biased by recognizability.
  • domain assumption LLM-generated numbered drawing plans are semantically suitable and parseable as conditioning text (Section 4.2).
    Uses GPT-5.2 for all planning; no systematic analysis of LLM failure modes is provided, and Section B.1 shows LLM plans for functions exceed the video model's rendering knowledge.
  • standard math Standard rectified flow matching objective is a valid fine-tuning loss for few-shot adaptation (Eq. 2).
    Background diffusion/flow-matching math reused without modification; not the paper's contribution.

pith-pipeline@v1.3.0-alltime-deepseek · 20612 in / 10306 out tokens · 104853 ms · 2026-08-02T22:42:59.085126+00:00 · methodology

0 comments
read the original abstract

Sketching is inherently sequential: strokes are drawn progressively to explore and refine ideas. Yet most generative approaches treat sketches as static images, ignoring the temporal process underlying creative exploration. Modeling this sequential structure remains challenging: prior methods either rely on large-scale human-drawn datasets with limited diversity, or use large language models (LLMs) to produce drawing instructions, often at the cost of visual fidelity. We present VideoSketcher, a method for generating high-quality sketching processes by adapting pretrained text-to-video diffusion models to the sparse, continuous nature of sketch formation. Our key insight is that LLMs and video diffusion models offer complementary strengths: LLMs act as semantic planners that decompose concepts into step-by-step instructions, while video diffusion models serve as powerful "renderers" that translate them into temporally coherent sketch sequences. We introduce a two-stage fine-tuning strategy that decouples temporal structure from visual appearance: stroke ordering is learned from synthetic shape compositions, while style is distilled from as few as seven hand-drawn examples. Despite minimal supervision, our method can generate diverse, high-quality sequential sketches that faithfully follow specified drawing orders. Our framework naturally extends to brush style control and autoregressive generation, supporting artistic applications.

Figures

Figures reproduced from arXiv: 2602.15819 by Alexander Schwing, Antonio Torralba, Hui Ren, Omer Bar Tal, Yael Vinker, Yuval Alaluf.

Figure 1
Figure 1. Figure 1: VideoSketcher enables sequential sketch generation in pixel space via video diffusion priors. Given a text prompt, our method generates a step-by-step [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SVG-to-video sketch representation. SVG paths are parsed and rendered sequentially into a video in which strokes are progressively drawn on a canvas. Each frame introduces at most one new stroke, ensuring clean temporal structure while enabling pixel-based video diffusion modeling. artist draws each sketch in Adobe Illustrator, which records both the stroke sequence and the drawing trajectory of each indiv… view at source ↗
Figure 3
Figure 3. Figure 3: Two-stage fine-tuning scheme. Left: synthetic sketches composed of simple geometric primitives teach drawing “grammar” and stroke or￾dering, independently of appearance. Right: a small set of human-drawn sketches of real-world objects adapts the model to a target visual style. 4.3 Two-Stage Finetuning While video diffusion models encode strong visual priors, they lack an intrinsic notion of meaningful draw… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results. Results generated with our fine-tuned video model. Full video results are provided in the supplementary materials. Because the shapes are visually simple and semantically neutral, this dataset minimizes appearance-related variability and encour￾ages the model to focus on learning temporal stroke ordering rather than object-specific visual details. Text prompts explicitly describe the i… view at source ↗
Figure 5
Figure 5. Figure 5: Diversity of sequential sketch generation. Top: two seeds for the same prompt; Bottom: the same object in different settings/actions [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: CLIP-based recognition over the sketching process. Our method gradually increases semantic recognizability as the sketch progresses, in contrast to baselines that either collapse temporal progression or attain lower recognition. Shaded areas indicate variance across samples [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Co-Drawing. Top: our interactive demo, where users draw alongside the model in real time. Bottom: turn-based co-drawing for “a creature.” used during evaluation, approximating the expected similarity to a randomly chosen brush. Our method achieves an average distance of 3.73 compared to 7.29 for the random baseline (a 49% reduction), indicating strong alignment with the target brush style. 5.4 Autoregressi… view at source ↗
Figure 10
Figure 10. Figure 10: Ablation study qualitative comparison. We compare sketching processes from models trained only on geometric primitives, only on real sketches, and the full two-stage model. The full model combines the strengths of both baselines, producing visually appealing sketches that follow the specified order. “An octopus” “A squirrel” “A penguin” [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Autoregressive results. Additional results from our autoregressive model, which enables interactive generation while maintaining visual quality comparable to diffusion-based results [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Limitations. (a) Multiple strokes may appear together (in red). (b) Model’s prior can override prompt. (c) Concepts outside the video model’s knowledge are incorrectly depicted. (d) Reduced quality for AR outputs. prior, it may deviate from the instructions. For example, in the “tiger roaring” prompt, the model changes the action late in the video and introduces color. Third, performance depends on the vi… view at source ↗
Figure 13
Figure 13. Figure 13: Additional results for brush style control. We show concepts drawn with seen (left) and unseen (right) brush styles and colors. “A beach at sunset” “A lighthouse on a rocky shore” “A medieval town square” “A shepherd with sheep” “A fisherman by a river” “A harbor city with ships” [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative results. Additional results generated with our fine-tuned text-to-video model. Full video results are provided in the supplementary [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Text-specified stroke ordering. Each row shows the same concept generated using a different text prompt that specifies a distinct drawing order. Wan 2.1 PaintsUndo SketchAgent Human Ours “A church” “A cow” [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Additional qualitative comparison of sequential sketch generation across methods. The Human drawings are taken from QuickDraw [Jonas et al. 2016]. Full video results are provided in the supplementary materials [PITH_FULL_IMAGE:figures/full_fig_p013_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Six brush styles (left) and 8 colors (right) used for training our [PITH_FULL_IMAGE:figures/full_fig_p014_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Scientific Concepts. Representative results for scientific concepts across methods. SketchAgent leverages LLM knowledge to capture concep￾tual structure (e.g., the interference pattern in double-slit, F=ma in Newton’s laws), though with limited visual detail. Wan 2.1 occasionally produces informative diagrams but often includes colored backgrounds and text labels rather than sketch-style outputs. Our meth… view at source ↗
Figure 19
Figure 19. Figure 19: Diagrams. Representative results for diagram concepts. SketchA￾gent produces structurally correct but visually minimal outputs. Wan 2.1 generates detailed diagrams with text and color, deviating from a sketch aesthetic. Our method captures the visual structure of diagrams (e.g., con￾nected nodes in flowcharts and mind maps, overlapping circles in Venn diagrams) with a cleaner sketch style, though text ele… view at source ↗
Figure 22
Figure 22. Figure 22: Functions. Representative results for mathematical functions. This category most clearly demonstrates the advantage of LLM-based ap￾proaches: SketchAgent produces precise, mathematically correct curves due to its language model backbone. Both Wan 2.1 and our method strug￾gle—curves are often incorrect or unrecognizable, and text/equations are garbled (e.g., 𝑦 = 𝑙𝑜𝑔(𝑥 ) in our output). This reveals a funda… view at source ↗
Figure 23
Figure 23. Figure 23: Accumulated ratio of newly added pixels as a function of video progress. Values are normalized such that the final frame equals 1. Our method exhibits a smooth and steady accumulation curve, reflecting incremental stroke additions over time and closely mirroring human drawing behavior [PITH_FULL_IMAGE:figures/full_fig_p018_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Demo of our interactive sketching interface. Users can co-draw with the model on the shared canvas for a concept. D Prompt Adherence We evaluate prompt adherence by progressively enriching an ini￾tially simple text instruction with additional details. As shown in [PITH_FULL_IMAGE:figures/full_fig_p018_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Prompt adherence with incremental details. Given a base concept (a house), we progressively add details in a prompt (e.g., with doors, with chimney and doors, with chimney, doors, and windows, and with chimney, doors, windows, and fence). The generated sketch sequences consistently follow the prompt and incorporate the newly requested elements [PITH_FULL_IMAGE:figures/full_fig_p019_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Training data exemplars of our simple geometric primitives. Examples of the simple geometric primitives used in the first stage of our fine-tuning pipeline, focused on teaching the model basic drawing “grammar” [PITH_FULL_IMAGE:figures/full_fig_p020_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Training data exemplars of our simple geometric primitives. Additional examples of the simple geometric primitives used in the first stage of our fine-tuning pipeline, focused on teaching the model basic drawing “grammar” [PITH_FULL_IMAGE:figures/full_fig_p021_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Our seven real human sketches used for training. We provide the seven human-drawn sketches used in the second stage of our fine-tuning pipeline [PITH_FULL_IMAGE:figures/full_fig_p022_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Additional text-to-video results (1/2). Additional qualitative results generated with our text-to-video model [PITH_FULL_IMAGE:figures/full_fig_p023_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Additional text-to-video results (2/2). Additional qualitative results generated with our text-to-video model [PITH_FULL_IMAGE:figures/full_fig_p024_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Additional brush control I2V results. We show results using an unseen brush style (caligraphy-vertical) and color (indigo-blue). “Girl riding a unicorn” “Robot at a mirror” “Amsterdam canal” “Lighthouse on rocks” “Rome street with a cat” [PITH_FULL_IMAGE:figures/full_fig_p025_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Additional brush control I2V results. We show results using an unseen brush style (hard-large-dots) and color (pink) [PITH_FULL_IMAGE:figures/full_fig_p025_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Additional brush control I2V results. We show results using an unseen brush style (bubbles) and color (mustard-olive). “Girl riding a unicorn” “Robot at a mirror” “Amsterdam canal” “Lighthouse on rocks” “Rome street with a cat” [PITH_FULL_IMAGE:figures/full_fig_p026_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: Additional brush control I2V results. We show results using an unseen brush style (caligraphy) and color (mocha-brown) [PITH_FULL_IMAGE:figures/full_fig_p026_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Autoregressive results. Additional autoregressive model results on QuickDraw prompts [PITH_FULL_IMAGE:figures/full_fig_p027_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: Autoregressive results. Additional autoregressive model results on QuickDraw prompts [PITH_FULL_IMAGE:figures/full_fig_p028_36.png] view at source ↗

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