Pith. sign in

REVIEW 3 major objections 3 minor 71 references

Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy

T0 review · 3 major / 3 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that a user can edit the hidden generating program of a pattern image without ever recovering that program: show the model two simple patterns (A, A′) that demonstrate the intended structural change, and it will apply the…

desk verdict Genuinely new problem framing and a clean system, but the best-of-k evaluation protocol overstates faithful performance; deserves review with demand for fixed-sample and per-style results. read the letter →

arxiv 2412.12463 v2 pith:XDMJ3N43 submitted 2024-12-17 cs.CV cs.AIcs.GRcs.HC

Aditya Ganeshan , Thibault Groueix , Paul Guerrero , Radomír Měch , Matthew Fisher , Daniel Ritchie This is my paper · ORCID
classification cs.CVcs.AIcs.GRcs.HC
keywords patterneditingprogrammaticimageeditsanalogieslatentdiffusionmodeldomain-specificlanguagesynthetictrainingdataanalogy-basedvisualprograminference
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

The paper argues that a user can edit the hidden generating program of a pattern image without ever recovering that program: show the model two simple patterns (A, A′) that demonstrate the intended structural change, and it will apply the same change to an arbitrary target pattern B. To make this learnable, the authors design a domain-specific language (SplitWeave) and a sampler that produces roughly a million synthetic analogy quartets (A, A′, B, B′) in which the program-level edit is identical between the two pairs. They then train TriFuser, a latent diffusion model conditioned on all three images, with three architectural fixes for token entanglement, semantic bias, and detail erosion. On a curated set of fifty artist-made patterns, human raters preferred TriFuser's outputs over three baselines, and the model also edits pattern styles never seen in training. The deeper claim is that structure-aware edits can be transferred by analogy from simple examples to complex images without program inference.

What carries the argument

SplitWeave, a domain-specific language whose programs generate patterns through canvas fragmentation, fragment-ID-aware operations, and SVG-style compositing. It carries the argument in two ways: its program samplers produce the synthetic analogy quartets used for training, and its parametric interface lets a user author the demonstration pair (A, A′) at test time. The second load-bearing piece is the program-level analogy relation R(zA, zA′) = R(zB, zB′), which defines what a correct edit means without any visual program inference. The third is TriFuser's conditioning stack: low- and high-level features from a text-image encoder and a self-supervised encoder are fused, and a 3D positional encoding marks each token's position and source image, which the ablation identifies as the most important component.

What would settle it

Construct synthetic quartets in which the program-level edit is identical between (A, A′) and (B, B′) but is visually imperceptible in A's rendering (for example, a 1-degree rotation of every tile); if the model cannot transfer such an edit even though Equation 1 holds, then the approach depends on the edit being legible in pixels, not on the program relation. A second check is a quantitative out-of-distribution benchmark where ground-truth edited targets for unseen real pattern styles are obtained by an independent program-inference oracle; if TriFuser's structural similarity to those targets is no better than an identity baseline, the claimed generalization to novel styles fails.

Watch

Extended reading notes

Core claim

The central claim is that programmatic image edits—changes to the rules that generate a pattern, such as swapping the tiling layout or inserting a new motif—can be specified by analogy and executed by a learned generative model, with no inference of the target's underlying program. The paper grounds the analogy in structure-mapping theory, requiring R(zA, zA′) = R(zB, zB′), i.e., the relationship between the two demonstration programs equals the relationship between the two target programs; the training quartets satisfy this by construction because the same edit operator is applied to both programs. The resulting model, TriFuser, is a latent diffusion model that takes patch tokens from A, A′, and B, fuses high- and low-level features from a text-aligned and a self-supervised encoder, and adds a 3D positional encoding (2D position plus which source image the token came from) so the network can tell which image to edit and which two images define the edit. The paper reports that TriFuser is preferred by human raters over training-free, latent-arithmetic, and inpainting baselines on real-world patterns, and that it generalizes to five pattern styles unseen during training. The direct corollary is that a designer can demonstrate an edit on a simple pattern and have it applied to a complex, real-world pattern while other structural features of the target are preserved.

Load-bearing premise

The load-bearing premise is that every edit the system needs to transfer is visible as a pixel-level difference between A and A′, and that a model trained on two synthetic pattern styles will read that difference in a way that transfers to unrelated real-world pattern styles.

Editorial extensions

If this is right

  • A designer can specify a structural edit on a simple pattern and have it applied to a complex, real-world pattern without having to write or infer any code.
  • The synthetic training data pipeline can be reused to train analogous editors for new pattern styles simply by adding corresponding program samplers.
  • Because the method preserves the target's other structural features, it enables practical tasks like mixing elements of two patterns and transferring pattern animations.
  • The architectural fixes for token entanglement, semantic bias, and detail erosion are presented as general lessons for conditioning diffusion models on multiple input images, not just for patterns.
  • The ability to generalize to unseen pattern styles suggests that the learned notion of analogy is at least partly style-independent.

Reading between the lines

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

  • Editorial extension: The program-level analogy relation suggests a harder test: if the same program edit can be rendered in two visually very different ways, a model trained only on rendered images may still learn the edit, but it is equally possible that it latches onto surface appearance; the paper does not isolate these two pathways.
  • Editorial extension: The pattern-mixing application could be pushed further into a compositional editing system in which multiple analogies are chained, so that a user builds a complex edit by sequentially applying several simple demonstrations.
  • Editorial extension: A direct quantitative out-of-distribution benchmark, where unseen real styles are paired with ground-truth program edits from an independent oracle, would sharpen the paper's generalization claim beyond the qualitative examples shown.
  • Editorial extension: The three conditioning fixes—especially the 3D positional encoding—could transfer to other multi-image conditioned diffusion tasks such as pose transfer or multi-view generation, although the paper does not test that transfer.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper proposes an analogy-based method for programmatic editing of pattern images without program inference. Given a pair (A, A') demonstrating an edit and a target pattern B, a latent diffusion model (TriFuser) generates B' that applies the demonstrated structural edit to B. To train this model, the authors introduce SplitWeave, a domain-specific language for generating synthetic pattern quartets with consistent program-level edits, and create a dataset of roughly 1 million samples across two synthetic styles (MTP and SFP). Evaluations include a human preference study on 50 real-world Adobe Stock patterns (aggregate preference rates 72-89% against three baselines), quantitative similarity metrics on a 1000-sample synthetic validation set, and a subtractive ablation of TriFuser's architectural components. The central claim is that the method faithfully performs demonstrated edits while generalizing to pattern styles beyond the two training styles.

Significance. The paper makes a solid contribution to pattern editing and visual analogy. The task formulation—performing programmatic edits by example without recovering the underlying program—is novel and practically motivated. SplitWeave is a reusable DSL and data-generation pipeline, and the synthetic quartet sampling procedure is well described. TriFuser's architectural modifications (3D positional encoding, multi-encoder feature fusion) are clearly motivated and validated by a clean subtractive ablation. The human study on 50 real-world patterns and the 1000-sample synthetic ground-truth evaluation are meaningful evaluation assets. If the evaluation-protocol concerns below are addressed, the paper would be a strong advance for structure-aware image editing.

major comments (3)
  1. [Section 4.3, Table 1] The human preference study aggregates results over all 50 real-world patterns, which include the two training styles (MTP, SFP) as well as five out-of-distribution styles. The abstract's claim of 'generalizing to related pattern styles beyond its training distribution' requires evidence that the preference advantage holds for the five styles not seen in training. Please report per-style preference rates, or at minimum the aggregate restricted to the out-of-distribution styles. Without this breakdown, the quantitative support for the OOD generalization claim is missing.
  2. [Section 4.4, Tables 2 and 3] The quantitative evaluation selects the best of k=5 generated outputs for each metric. This measures an upper envelope of performance, not the typical behavior of the model. The abstract claims the method 'faithfully performs the demonstrated edit'; to support this, please also report fixed-sample evaluation (e.g., metrics on the first generated sample, or mean/standard deviation over the k samples). The current protocol could mask frequent failures that users would encounter when sampling once.
  3. [Section 4.1 and Section 4.4] The synthetic validation set is generated from the same SplitWeave samplers as the training data, so it cannot test generalization to pattern styles beyond the two synthetic training distributions. The out-of-distribution claim therefore rests solely on the qualitative examples in Figure 7 and the aggregate human study. A per-style quantitative evaluation on out-of-distribution data (e.g., using the annotated real-world edits or additional synthetic styles) would directly test the generalization claim and should be added.
minor comments (3)
  1. [Section 4.2] The Inpainter baseline description is ambiguous: 'inpainting the lower-left quadrant of a 2x2 analogy grid containing (A, A', B)' does not specify whether the target output is placed in that quadrant and what the conditioning text template is. Please clarify the exact input arrangement.
  2. [Table 2 and Section 4.4] The metric is written 'DIST' in the text but 'DISTS' in the table header; the cited reference [7] is DISTS. Please unify the notation.
  3. [Throughout] The model name 'TRIFUSER' is inconsistently rendered as 'T RIFUSER' (with a space) in several places, including Figure 2 and Figure 5 captions. Please standardize.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the analogy-transfer claim is learned and evaluated on held-out data, not derived from a fitted equation or a self-citation chain.

full rationale

The paper's derivation chain is not circular. Equation (1), R(zA,zA') = R(zB,zB'), is used as a data-generation invariant: the authors sample an edit operator e and apply it to both zA and zB, so the program-level relation holds by construction. This does not smuggle in the model's output; TRIFUSER never sees the programs zA, zA', zB, or zB' during training or inference, only rendered images (A, A', B), and must learn the edit from pixels. The central claim is therefore an empirical generalization claim, tested on a held-out synthetic validation set (Section 4.4, Table 2) and on 50 real-world Adobe Stock patterns with a human preference study (Section 4.3, Table 1). Self-citations appear only as background in Related Work (e.g., [13,14,31]) or as standard initialization from Versatile Diffusion [65]; none is invoked as an external theorem that forces the method's choice. The paper itself flags the two main evaluation approximations: program-level verification is infeasible, so outputs are compared with perceptual metrics (Section 4.4), and the best-of-k selection (k=5 for synthetic, k=9 for real) means the tables measure whether at least one sample matches the intended edit rather than typical behavior. These are limitations on the strength of the empirical evidence, not circular steps; no equation or fitted parameter is renamed as a prediction. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the visual legibility of program-level edits, the representativeness of the synthetic distribution, and the validity of perceptual proxies for program verification. These are reasonable domain assumptions, but none is proven independently.

free parameters (4)
  • Best-of-k evaluation selection (k=9 human study, k=5 synthetic) = 9 / 5
    The evaluation protocol chooses the best of k generated outputs before measuring preference or similarity; this hand-chosen procedure inflates apparent performance and is not part of the model itself.
  • Classifier-free guidance weight = 7.5
    Standard diffusion sampling hyperparameter, selected without a reported sweep; it affects the balance between edit fidelity and diversity.
  • Denoising steps = 50
    Inference-time hyperparameter; a standard default for latent diffusion models.
  • Edit operator set E (insertion, removal, replacement)
    The scope of programmatic edits the model can perform is fixed by the hand-defined operators in SplitWeave; any claim about programmatic edits is limited to this vocabulary.
assumptions (4)
  • domain assumption SplitWeave patterns (MTP and SFP) are representative enough to support transfer to real-world patterns.
    The entire training signal is synthetic; the claimed real-world generalization depends on this assumption, which is not proven by any data distribution analysis.
  • domain assumption Program-level edits are visually legible in rendered images, so an image-conditioned model can infer them.
    Eq. (1) defines analogies on programs, but the model only sees pixels; if the edit is not visually apparent in (A, A'), the training objective cannot convey it.
  • domain assumption Perceptual metrics and human preference are valid proxies for programmatic edit correctness.
    Section 4.4 states that verifying the underlying program of B' is infeasible, so image similarity is used as a substitute; this substitution is assumed valid.
  • domain assumption The 50 Adobe Stock patterns across 7 styles form a representative real-world benchmark.
    Section 4.1 describes the curation, but no statistics show that the set is representative of the broader distribution of pattern images.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy." pith.science (2026). https://pith.science/paper/XDMJ3N43

@misc{pith2026241212463,
  author       = {Pith},
  title        = {Pith review of: Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XDMJ3N43}},
  note         = {Machine review of arXiv:2412.12463}
}
read the original abstract

Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often programmatic: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but current methods for doing so struggle with complex images and produce unorganized programs that make editing tedious. In this work, we introduce a novel approach to perform programmatic edits on pattern images. By using a pattern analogy -- a pair of simple patterns to demonstrate the intended edit -- and a learning-based generative model to execute these edits, our method allows users to intuitively edit patterns. To enable this paradigm, we introduce SplitWeave, a domain-specific language that, combined with a framework for sampling synthetic pattern analogies, enables the creation of a large, high-quality synthetic training dataset. We also present TriFuser, a Latent Diffusion Model (LDM) designed to overcome critical issues that arise when naively deploying LDMs to this task. Extensive experiments on real-world, artist-sourced patterns reveals that our method faithfully performs the demonstrated edit while also generalizing to related pattern styles beyond its training distribution.

Figures

Figures reproduced from arXiv: 2412.12463 by the authors.

Figure 1
Figure 1. Our system performs programmatic edits on pattern images without inferring their underlying programs. (Left) Desired edits, expressed with a pair of patterns (A, A′ ), are executed on a target pattern B by a generative model to produce B ′ . (Right) Parametric changes A → A ′ enabled by our domain-specific pattern language induce corresponding changes to the more complex pattern B. Abstract Pattern images are everyw… view at source ↗
Figure 2
Figure 2. Overview: To create high-quality visual patterns, we introduce a custom DSL called SPLITWEAVE. Pairs of SPLITWEAVE programs (A, B) are then jointly edited to create analogical quartets. This synthetic data is then used to train TRI￾FUSER, a neural network for analogical pattern editing. pearance edits, often failing to perform programmatic edits. This limitation arises both from the models’ architectures and from th… view at source ↗
Figure 4
Figure 4. We create synthetic analogical quartets (A, A′ , B, B′ ) with consistent edits between A and B pairs, providing data for training an analogical editing models. scaling alternating rows or columns) to support spatial vari￾ability in non-stationary pattern designs; and (3) various SVG Operators for outlining, coloring, and compositing. Together, these operations enable efficient creation of pat￾terns with complex stru… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: (Left) TRIFUSER is a latent diffusion model conditioned on patch-wise tokens of the input images (A, A′ , B) to generate the analogically edited pattern B ′ . (Right) To achieve high-quality edits, we enrich these tokens by fusing multi-level features from multiple enc…
Figure 6
Figure 6. Figure 6: Qualitative comparison between patterns generated by [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: TRIFUSER effectively edits patterns from novel pattern styles not present in the training dataset. TRIFUSER shows a note￾worthy ability to generalize beyond its training distribution. painting model. This model, which we term Inpainter, per￾forms analogical editing by …
Figure 8
Figure 8. Figure 8: Our model helps users mix elements of different real [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

71 extracted references · 63 canonical work pages

  1. [1]

    Cross-image attention for zero- shot appearance transfer

    Yuval Alaluf, Daniel Garibi, Or Patashnik, Hadar Averbuch- Elor, and Daniel Cohen-Or. Cross-image attention for zero- shot appearance transfer. In ACM SIGGRAPH 2024 Con- ference Papers, New York, NY , USA, 2024. Association for Computing Machinery. 3

    work page 2024

  2. [2]

    Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Glober- son, and Alexei A. Efros. Visual prompting via image in- painting. arXiv preprint arXiv:2209.00647, 2022. 2, 3

    arXiv 2022

  3. [3]

    Loosec- ontrol: Lifting controlnet for generalized depth conditioning

    Shariq Farooq Bhat, Niloy Mitra, and Peter Wonka. Loosec- ontrol: Lifting controlnet for generalized depth conditioning. In ACM SIGGRAPH 2024 Conference Papers , New York, NY , USA, 2024. Association for Computing Machinery. 3

    work page 2024

  4. [4]

    Intrinsic decompositions for image editing

    Nicolas Bonneel, Balazs Kovacs, Sylvain Paris, and Kavita Bala. Intrinsic decompositions for image editing. Com- puter Graphics Forum (Eurographics State of the Art Reports 2017), 36(2), 2017. 3

    work page 2017

  5. [5]

    Emerg- ing properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerg- ing properties in self-supervised vision transformers. In Pro- ceedings of the International Conference on Computer Vi- sion (ICCV), 2021. 6

    2021

  6. [6]

    Learning continuous 3d words for text-to- image generation

    Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, and Niki Trigoni. Learning continuous 3d words for text-to- image generation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 6753–6762, 2024. 3

    work page 2024

  7. [7]

    Simoncelli

    Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Image quality assessment: Unifying structure and texture similarity. CoRR, abs/2004.07728, 2020. 8

    arXiv 2004

  8. [8]

    Image vectorization and editing via lin- ear gradient layer decomposition

    Zheng-Jun Du, Liang-Fu Kang, Jianchao Tan, Yotam Gin- gold, and Kun Xu. Image vectorization and editing via lin- ear gradient layer decomposition. ACM Trans. Graph., 42 (4), 2023. 3

    work page 2023

Show all 71 references
  1. [9]

    Learning to infer graphics programs from hand-drawn images

    Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum. Learning to infer graphics programs from hand-drawn images. In Advances in Neural Information Pro- cessing Systems. Curran Associates, Inc., 2018. 2

    2018

  2. [10]

    Thomas G. Evans. A heuristic program to solve geometric- analogy problems. In Proceedings of the April 21-23, 1964, Spring Joint Computer Conference , page 327–338, New York, NY , USA, 1964. Association for Computing Machin- ery. 3

    1964

  3. [11]

    Forbus, and Dedre Gentner

    Brian Falkenhainer, Kenneth D. Forbus, and Dedre Gentner. The structure-mapping engine. In Proceedings of the Fifth AAAI National Conference on Artificial Intelligence , page 272–277. AAAI Press, 1986. 3

    1986

  4. [12]

    Dream- sim: Learning new dimensions of human visual similarity using synthetic data

    Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dream- sim: Learning new dimensions of human visual similarity using synthetic data. Advances in Neural Information Pro- cessing Systems, 36, 2024. 8

    2024

  5. [13]

    Kenny Jones, and Daniel Ritchie

    Aditya Ganeshan, R. Kenny Jones, and Daniel Ritchie. Im- proving unsupervised visual program inference with code rewriting families. In Proceedings of the International Con- ference on Computer Vision (ICCV), 2023. 3

    2023

  6. [14]

    Huang, Xianghao Xu, R

    Aditya Ganeshan, Ryan Y . Huang, Xianghao Xu, R. Kenny Jones, and Daniel Ritchie. Parsel: Parameterized shape edit- ing with language, 2024. 3

    2024

  7. [15]

    Computer-aided design as language

    Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, and Stefano Saliceti. Computer-aided design as language. In Advances in Neural Information Processing Systems , pages 5885–5897. Curran Associates, Inc., 2021. 3

    2021

  8. [16]

    Gatys, Alexander S

    Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. A neural algorithm of artistic style. CoRR, abs/1508.06576,

    arXiv

  9. [17]

    Structure-mapping: A theoretical framework for analogy

    Dedre Gentner. Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2):155–170, 1983. 5

    1983

  10. [18]

    Analogist: Out-of-the-box visual in-context learning with image diffusion model

    Zheng GU, Shiyuan Yang, Jing Liao, Jing Huo, and Yang Gao. Analogist: Out-of-the-box visual in-context learning with image diffusion model. ACM Transactions on Graphics (TOG), 2024. 3, 7

    2024

  11. [19]

    Guehl, R

    P. Guehl, R. All `egre, J.-M. Dischler, B. Benes, and E. Galin. Semi-procedural textures using point process texture basis 9 functions. Computer Graphics Forum, 39(4):159–171, 2020. 3

    2020

  12. [20]

    Texsliders: Diffusion- based texture editing in clip space

    Julia Guerrero-Viu, Milos Hasan, Arthur Roullier, Midhun Harikumar, Yiwei Hu, Paul Guerrero, Diego Guti ´errez, Be- len Masia, and Valentin Deschaintre. Texsliders: Diffusion- based texture editing in clip space. InACM SIGGRAPH 2024 Conference Papers, New York, NY , USA, 2024....

    2024

  13. [21]

    Prompt-to-prompt image editing with cross attention control

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. 2022. 3

    2022

  14. [22]

    Jacobs, Nuria Oliver, Brian Curless, and David H

    Aaron Hertzmann, Charles E. Jacobs, Nuria Oliver, Brian Curless, and David H. Salesin.Image Analogies. Association for Computing Machinery, New York, NY , USA, 1 edition,

  15. [23]

    Denoising diffu- sion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models. In Proceedings of the 34th Inter- national Conference on Neural Information Processing Sys- tems, Red Hook, NY , USA, 2020. Curran Associates Inc. 5

    2020

  16. [24]

    The Copycat project: a model of mental fluidity and analogy-making, page 205–267

    Douglas Hofstadter and Melanie Mitchell. The Copycat project: a model of mental fluidity and analogy-making, page 205–267. Basic Books, Inc., USA, 1995. 3

    1995

  17. [25]

    Node graph optimization using differentiable proxies

    Yiwei Hu, Paul Guerrero, Milos Hasan, Holly Rushmeier, and Valentin Deschaintre. Node graph optimization using differentiable proxies. In ACM SIGGRAPH Conference Pro- ceedings, 2022. 3

    2022

  18. [26]

    From CLIP to DINO: Visual encoders shout in multi-modal large language models, 2024

    Dongsheng Jiang, Yuchen Liu, Songlin Liu, XIAOPENG ZHANG, Jin Li, Hongkai Xiong, and Qi Tian. From CLIP to DINO: Visual encoders shout in multi-modal large language models, 2024. 6

    2024

  19. [27]

    Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy J

    R. Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy J. Mitra, and Daniel Ritchie. Shapeassembly: Learning to generate programs for 3d shape structure synthesis. ACM Transactions on Graphics (TOG), Siggraph Asia 2020, 2020. 3

    2020

  20. [28]

    Kenny Jones, David Charatan, Paul Guerrero, Niloy J

    R. Kenny Jones, David Charatan, Paul Guerrero, Niloy J. Mi- tra, and Daniel Ritchie. Shapemod: Macro operation discov- ery for 3d shape programs. ACM Transactions on Graphics (TOG), Siggraph 2021, 2021. 3

    2021

  21. [29]

    Kenny Jones, Homer Walke, and Daniel Ritchie

    R. Kenny Jones, Homer Walke, and Daniel Ritchie. Plad: Learning to infer shape programs with pseudo-labels and ap- proximate distributions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 3

    2022

  22. [30]

    Kenny Jones, Paul Guerrero, Niloy J

    R. Kenny Jones, Paul Guerrero, Niloy J. Mitra, and Daniel Ritchie. Shapecoder: Discovering abstractions for visual programs from unstructured primitives. ACM Transactions on Graphics (TOG), Siggraph 2023, 42(4), 2023. 3

    2023

  23. [31]

    Kenny Jones, Renhao Zhang, Aditya Ganeshan, and Daniel Ritchie

    R. Kenny Jones, Renhao Zhang, Aditya Ganeshan, and Daniel Ritchie. Learning to edit visual programs with self- supervision. In Advances in Neural Information Processing Systems, 2024. 3

    2024

  24. [32]

    Reparamcad: Zero-shot cad re-parameterization for interactive manipula- tion

    Milin Kodnongbua, Benjamin Jones, Maaz Bin Safeer Ah- mad, Vladimir Kim, and Adriana Schulz. Reparamcad: Zero-shot cad re-parameterization for interactive manipula- tion. In SIGGRAPH Asia 2023 Conference Papers , New York, NY , USA, 2023. Association for Computing Machin- ery. 3

    2023

  25. [33]

    Ebdm: Exemplar-guided image translation with brownian-bridge diffusion models

    Eungbean Lee, Somi Jeong, and Kwanghoon Sohn. Ebdm: Exemplar-guided image translation with brownian-bridge diffusion models. In Computer Vision – ECCV 2024, pages 306–323, Cham, 2025. Springer Nature Switzerland. 3

    2024

  26. [34]

    End-to-end procedural material capture with proxy-free mixed-integer optimization

    Beichen Li, Liang Shi, and Wojciech Matusik. End-to-end procedural material capture with proxy-free mixed-integer optimization. ACM Trans. Graph., 42(4), 2023. 3

    2023

  27. [35]

    Changjian Li, Hao Pan, Adrien Bousseau, and Niloy J. Mi- tra. Sketch2cad: Sequential cad modeling by sketching in context. ACM Trans. Graph. (Proceedings of SIGGRAPH Asia 2020), 39(6):164:1–164:14, 2020. 3

    2020

  28. [36]

    Dunn, Hao Tang, Michelan- gelo Naim, Dat Nguyen, Wei-Long Zheng, Zenna Tavares, Yewen Pu, and Kevin Ellis

    Wen-Ding Li, Keya Hu, Carter Larsen, Yuqing Wu, Simon Alford, Caleb Woo, Spencer M. Dunn, Hao Tang, Michelan- gelo Naim, Dat Nguyen, Wei-Long Zheng, Zenna Tavares, Yewen Pu, and Kevin Ellis. Combining induction and trans- duction for abstract reasoning, 2024. 4

    2024

  29. [37]

    Visual attribute transfer through deep image analogy

    Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang. Visual attribute transfer through deep image analogy. ACM Trans. Graph., 36(4), 2017. 3

    2017

  30. [38]

    Programmable 2d arrangements for element texture de- sign

    Hugo Loi, Thomas Hurtut, Romain Vergne, and Joelle Thol- lot. Programmable 2d arrangements for element texture de- sign. ACM Trans. Graph., 36(4), 2017. 3, 4

    2017

  31. [39]

    One noise to rule them all: Learning a unified model of spatially- varying noise patterns

    Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir Kim, Matthew Fisher, Soeren Pirk, and Daniel Ritchie. One noise to rule them all: Learning a unified model of spatially- varying noise patterns. ACM Trans. Graph. , 43(4), 2024. 3

    2024

  32. [40]

    Free- man, Joshua B

    Jiayuan Mao, Xiuming Zhang, Yikai Li, William T. Free- man, Joshua B. Tenenbaum, and Jiajun Wu. Program-Guided Image Manipulators. In International Conference on Com- puter Vision, 2019. 2

    2019

  33. [41]

    McCarthy, C

    L. McCarthy, C. Reas, and B. Fry.Getting Started with P5.js: Making Interactive Graphics in JavaScript and Processing . Maker Media, Incorporated, 2015. 4

    2015

  34. [42]

    Instructgie: Towards generalizable image editing

    Zichong Meng, Changdi Yang, Jun Liu, Hao Tang, Pu Zhao, and Yanzhi Wang. Instructgie: Towards generalizable image editing. arXiv preprint arXiv:2403.05018, 2024. 3

    2024 arXiv

  35. [43]

    George A. Miller. WordNet: A lexical database for En- glish. In Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994,

    1994

  36. [44]

    Editable image elements for controllable synthesis

    Jiteng Mu, Micha ¨el Gharbi, Richard Zhang, Eli Shecht- man, Nuno Vasconcelos, Xiaolong Wang, and Taesung Park. Editable image elements for controllable synthesis. arXiv preprint arXiv:2404.16029, 2024. 3

    2024 arXiv

  37. [45]

    Gpt-4 technical report, 2024

    OpenAI. Gpt-4 technical report, 2024. 3

    2024

  38. [46]

    Maxime Oquab, Timoth ´ee Darcet, Th´eo Moutakanni, Huy V . V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael ...

    2024

  39. [47]

    Yeh, and Rana Hanocka

    Ofek Pearl, Itai Lang, Yuhua Hu, Raymond A. Yeh, and Rana Hanocka. Geocode: Interpretable shape programs. 2022. 3 10

    2022

  40. [48]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Proceedings of th...

    2021

  41. [49]

    Deep visual analogy-making

    Scott Reed, Yi Zhang, Yuting Zhang, and Honglak Lee. Deep visual analogy-making. In Proceedings of the 28th Inter- national Conference on Neural Information Processing Sys- tems - Volume 1 , page 1252–1260, Cambridge, MA, USA,

  42. [50]

    Csg-stump: A learning friendly csg-like representation for interpretable shape parsing

    Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Haiyong Jiang, Zhongang Cai, Junzhe Zhang, Liang Pan, Mingyuan Zhang, Haiyu Zhao, and Shuai Yi. Csg-stump: A learning friendly csg-like representation for interpretable shape parsing. In Proceedings of the IEEE/CVF Interna- t...

    2021

  43. [51]

    Extrudenet: Unsupervised inverse sketch- and-extrude for shape parsing

    Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, and Junzhe Zhang. Extrudenet: Unsupervised inverse sketch- and-extrude for shape parsing. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II , page 482–498, B...

    2022

  44. [52]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022. 2, 5, 7

    2022

  45. [53]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , pages 234–241, Cham, 2015. Springer International Publishing. 5

    2015

  46. [54]

    Geodiffuser: Geometry-based image editing with diffusion models, 2024

    Rahul Sajnani, Jeroen Vanbaar, Jie Min, Kapil Katyal, and Srinath Sridhar. Geodiffuser: Geometry-based image editing with diffusion models, 2024. 3

    2024

  47. [55]

    gtangle: a grammar for the procedural generation of tangle patterns

    Christian Santoni and Fabio Pellacini. gtangle: a grammar for the procedural generation of tangle patterns. ACM Trans. Graph., 35(6), 2016. 3, 4

    2016

  48. [56]

    Match: differentiable material graphs for procedural mate- rial capture

    Liang Shi, Beichen Li, Milo ˇs Ha ˇsan, Kalyan Sunkavalli, Tamy Boubekeur, Radomir Mech, and Wojciech Matusik. Match: differentiable material graphs for procedural mate- rial capture. ACM Trans. Graph., 39(6), 2020. 2, 3

    2020

  49. [57]

    Imagebrush: learning visual in-context instructions for exemplar-based image manipulation

    Yasheng Sun, Yifan Yang, Houwen Peng, Yifei Shen, Yuqing Yang, Han Hu, Lili Qiu, and Hideki Koike. Imagebrush: learning visual in-context instructions for exemplar-based image manipulation. In Proceedings of the 37th Interna- tional Conference on Neural Information Processing ...

    2024

  50. [58]

    Zero-shot image-to-text generation for visual-semantic arith- metic

    Yoad Tewel, Yoav Shalev, Idan Schwartz, and Lior Wolf. Zero-shot image-to-text generation for visual-semantic arith- metic. arXiv preprint arXiv:2111.14447, 2021. 7

    2021 arXiv

  51. [59]

    Eyes wide shut? exploring the visual shortcomings of multimodal llms, 2024

    Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms, 2024. 6

    2024

  52. [60]

    Solving olympiad geometry without human demon- strations

    Trieu Trinh, Yuhuai Tony Wu, Quoc Le, He He, and Thang Luong. Solving olympiad geometry without human demon- strations. Nature, 625:476–482, 2024. 4

    2024

  53. [61]

    Diffusion image analo- gies

    Ad ´ela ˇSubrtov´a, Michal Luk ´aˇc, Jan ˇCech, David Futschik, Eli Shechtman, and Daniel S ´ykora. Diffusion image analo- gies. In ACM SIGGRAPH 2023 Conference Proceedings , New York, NY , USA, 2023. Association for Computing Ma- chinery. 2, 3, 7

    2023

  54. [62]

    Edict: Ex- act diffusion inversion via coupled transformations

    Bram Wallace, Akash Gokul, and Nikhil Naik. Edict: Ex- act diffusion inversion via coupled transformations. arXiv preprint arXiv:2211.12446, 2022. 3

    2022 arXiv

  55. [63]

    Deepcad: A deep generative network for computer-aided design models

    Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 6772–6782, 2021. 3

    2021

  56. [64]

    Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl D. D. Willis, and Daniel Ritchie. Inferring cad modeling sequences using zone graphs. In CVPR, 2021. 3

    2021

  57. [65]

    Versatile diffusion: Text, images and variations all in one diffusion model

    Xingqian Xu, Zhangyang Wang, Gong Zhang, Kai Wang, and Humphrey Shi. Versatile diffusion: Text, images and variations all in one diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7754–7765, 2023. 2, 5, 6, 7, 8

    2023

  58. [66]

    Diffusion models: A comprehensive survey of methods and applications

    Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Run- sheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming- Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56(4): 1–39, 2023. 5

    2023

  59. [67]

    Hodgins, and Levent Burak Kara

    Mehmet Ersin Yumer, Siddhartha Chaudhuri, Jessica K. Hodgins, and Levent Burak Kara. Semantic shape editing us- ing deformation handles. ACM Trans. Graph., 34(4), 2015. 3

    2015

  60. [68]

    Sigmoid loss for language image pre-training

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 11975–11986, 2023. 7

    2023

  61. [69]

    Transparent im- age layer diffusion using latent transparency

    Lvmin Zhang and Maneesh Agrawala. Transparent im- age layer diffusion using latent transparency. ACM Trans. Graph., 43(4), 2024. 6

    2024

  62. [70]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 8

    2018

  63. [71]

    Unpaired image-to-image translation using cycle- consistent adversarial networks

    Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle- consistent adversarial networks. In Computer Vision (ICCV), 2017 IEEE International Conference on, 2017. 3 11

    2017

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.