mlsanityData to 7 Oct 2026

Generating Sketches in a Hierarchical Auto-Regressive Process for Flexible Sketch Drawing Manipulation at Stroke-Level

arXiv 2511.07889 ↗First posted 11 Nov 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

Generating sketches with specific patterns as expected, i.e., manipulating sketches in a controllable way, is a popular task. Recent studies control sketch features at stroke-level by editing values of stroke embeddings as conditions. However, in order to provide generator a global view about what a sketch is going to be drawn, all these edited conditions should be collected and fed into generator simultaneously before generation starts, i.e., no further manipulation is allowed during sketch generating process. In order to realize sketch drawing manipulation more flexibly, we propose a hierarchical auto-regressive sketch generating process. Instead of generating an entire sketch at once, each stroke in a sketch is generated in a three-staged hierarchy: 1) predicting a stroke embedding to represent which stroke is going to be drawn, and 2) anchoring the predicted stroke on the canvas, and 3) translating the embedding to a sequence of drawing actions to form the full sketch. Moreover, the stroke prediction, anchoring and translation are proceeded auto-regressively, i.e., both the recently generated strokes and their positions are considered to predict the current one, guiding model to produce an appropriate stroke at a suitable position to benefit the full sketch generation. It is flexible to manipulate stroke-level sketch drawing at any time during generation by adjusting the exposed editable stroke embeddings.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

6 of 19 references.

References · 6

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Pay Attention to MLPscited by 367
  3. 2017A Neural Representation of Sketch Drawingscited by 311
  4. 2023ChiroDiff: Modelling chirographic data with Diffusion Modelscited by 14
  5. 2022Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representationcited by 4
  6. 2024Equipping Sketch Patches with Context-Aware Positional Encoding for Graphic Sketch Representationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.