Pith. sign in

REVIEW 4 major objections 3 minor 77 references

Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI

T0 review · 4 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read A generative-AI character prototyping tool gives non-artist game designers a concrete visual brief to hand to illustrators, and a study finds it beats sketching alone on creativity support.

desk verdict Sketchar's real finding is that non-artist designers feel more expressive with it; the collaboration benefit is asserted, not shown. read the letter →

arxiv 2508.12333 v1 pith:NHUR5WV5 submitted 2025-08-17 cs.HC

Long Ling , Xinyi Chen , Ruoyu Wen , Toby Jia-Jun Li , Ray LC This is my paper · ORCID
classification cs.HC
keywords gamecharacterdesigngenerativeAIcreativitysupportindexhuman-AIcollaborationtext-to-imagegenerationdesigner-illustratorcommunicationrapidprototypinglargelanguagemodels
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

This paper claims that a generative-AI tool can close a specific communication gap in game development: the gap between designers, who think in narrative and mechanics, and illustrators, who work in visual form. The tool, Sketchar, turns a designer's natural-language description into a structured character profile and a set of reference images, giving the designer something visual to hand to an illustrator even when the designer cannot draw. In a mixed-method study, designers rated the Sketchar workflow significantly higher than a sketching-only baseline on the Creativity Support Index, especially on collaboration, and designers without artistic backgrounds rated it higher than artists did. The paper concludes that such reference images refine design details and can enter real-world design workflows.

What carries the argument

The load-bearing mechanism is a hierarchical content-generation pipeline driven by prompt engineering. In the first layer, a large language model takes the designer's raw input (name, role, background story, game type) and summarizes it into integrated information; in the second layer it distills keywords; in the third layer those keywords, plus a chosen render style and role details, are sent to a text-to-image model that produces five reference images. A human-in-the-loop step lets the designer edit the profile or regenerate, and the result is packaged as a shareable character "ID card," with optional simulated conversations and a character family tree to support exploration. The identity doing the work is this translation from narrative intent to visual prototype through an intermediate keyword representation, which is also what makes the artifact legible to an illustrator.

What would settle it

Run the same character-design task with pairs of real designers and illustrators, giving half of the pairs a sketching-only brief and half a Sketchar brief, and count revision rounds and requirement mismatches until the illustrator's final asset is approved; if Sketchar does not reduce the number of iterations or mismatches, the claimed communication-bridge benefit is not supported.

Watch

Extended reading notes

Core claim

The central claim is that a designer who can describe a character in words can, with Sketchar's help, produce both a structured design document and a set of reference images that an illustrator can use as a starting point, and that this capability measurably improves the creative-design experience over sketching alone. The evidence is a two-part study: with 13 designers in qualitative sessions and 17 in a within-subjects quantitative comparison, Sketchar's Creativity Support Index total significantly exceeded the baseline (p = .028), with the Collaboration dimension the highest factor; non-artists scored Sketchar significantly higher than artists did (p = .011), especially on Expressiveness and Results Worth Effort. Five professional illustrators judged, on average, 6.84 of 10 images in each generated set as relevant reference material for real game character development. The paper frames this as a new application of generative AI: not replacing the illustrator, but giving the non-artist designer a concrete visual object to think with and hand off.

Load-bearing premise

The key claim rests on participants' imagined collaboration with an illustrator; designers were told to imagine the handoff rather than work with an actual illustrator, and if that imagined handoff does not resemble real designer-illustrator iteration, the communication-bridge benefit would not be established.

Editorial extensions

If this is right

  • A designer who cannot draw can nevertheless hand an illustrator a concrete visual brief, changing the handoff from words-and-hope to image-plus-document.
  • Because the Collaboration dimension drove the Creativity Support Index gain, designers perceive the tool as supporting the designer-illustrator relationship even when no illustrator is physically present.
  • With expert illustrators accepting most generated images as references, the images can plausibly enter professional pipelines as early-stage prototypes rather than as final art.
  • The structured profile generated alongside the image gives the design team a standardized character document, addressing the complaint that design briefs vary in format and clarity.
  • The tool lends itself to batching near-identical NPC variants, an efficiency the paper identifies as a natural next use.

Reading between the lines

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

  • Beyond the paper, the same two-stage "words to keywords to image" pipeline could be tested in other interdisciplinary handoffs, such as UX designer to developer or writer to animator, where one party lacks the other's medium.
  • Because the artist/non-artist gap was large, a testable prediction is that Sketchar's adoption and measured benefit will scale with the skill distance between the designer and the illustrator, not with overall team size.
  • The paper leaves to future work a direct comparison with a raw LLM-plus-image-generator workflow; the natural experiment is to see whether the structured profile and ID-card packaging add value over the same models used directly.
  • If real designer-illustrator pairs are used, the Collaboration subscale should be validated against an objective outcome such as number of revision rounds or illustrator comprehension score, since the Creativity Support Index collaboration scale normally assumes synchronous collaboration.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper presents Sketchar, a web-based generative-AI tool that combines ChatGPT for structuring character profiles and DALL-E for generating reference images, aimed at helping game designers who lack illustration skills communicate character concepts to illustrators. The authors report formative interviews with 10 game design professionals, a qualitative study with 13 game designers, a quantitative study with 17 participants comparing Sketchar against a baseline using the Creativity Support Index (CSI), and an expert evaluation with 5 illustrators who rated generated images as reference material. The main reported findings are that the CSI score for Sketchar was significantly higher than for the baseline (p = .028), that the Collaboration dimension scored highest, that designers without artistic backgrounds rated Sketchar higher than those with artistic backgrounds (p = .011), and that expert illustrators deemed an average of 6.84 out of 10 generated images per prompt usable as references. The paper claims that Sketchar's generated reference images foster refinement of design details and can be incorporated into real-world workflows.

Significance. If the claims are supported, the paper addresses a real and under-served problem: facilitating communication in game character design across the designer-illustrator divide. The formative interview grounding, the mixed-method design, the randomized task order in the quantitative study, and the candid acknowledgement of limitations are strengths. The qualitative findings (KF1-KF3) provide plausible, concrete evidence that non-artist designers feel better able to express and refine character ideas with generated references. The non-artist expressiveness result is a useful contribution. However, the headline communication-bridge claim rests on a CSI Collaboration subscale administered under an imagined asynchronous handoff and on an expert evaluation that did not involve a designer-illustrator workflow; as written, those data support perceived usefulness of generated images, not demonstrated improvement in cross-role collaboration.

major comments (4)
  1. [§4.4 and §5.5] The central collaboration claim is not supported by the reported data. The quantitative anchor for the communication-bridge contribution is the CSI Collaboration dimension, which 'scored higher than any other dimension' (§4.4), but the collaboration subscale is designed for synchronous collaboration with another person, and the authors told participants to imagine handing images to an illustrator. The paper's own §5.5 states that 'the collaboration had to be imagined to be taking place' and that 'the perception that the tool supports collaboration may be speculative.' Because no designer-illustrator pair worked together, the significant overall CSI difference and the high Collaboration subscale do not establish that Sketchar actually improves cross-role communication; they establish that participants perceived it might.
  2. [§4.5] The expert evaluation does not test a real-world workflow. Five illustrators rated pre-selected generated images and found an average of 6.84 of 10 images per set suitable as references, but they did not receive the designers' design documents, were not given feedback or revision requests, and did not interact with the designers. This procedure can show that experienced illustrators see some utility in the images, but it cannot support the abstract's stronger claim that the images 'can be incorporated into real-world workflows.' A workflow claim requires at least one complete designer-to-illustrator handoff, iteration, or negotiation.
  3. [§4.2.2 and Abstract] The baseline condition is described inconsistently, and this weakens the causal interpretation of the p = .028 CSI result. The abstract and introduction describe the control as a 'sketching-only' baseline, but the methods section says participants were asked to 'submit a character design proposal to the artist based on their experience and abilities' (§4.1.2 and §4.2.2), and §5.5 refers to the control as 'participants' original workflows.' If the baseline was not constrained to sketching and was not matched in structure, the significant CSI difference may reflect any structured digital workflow support rather than the specific GenAI-mediated features of Sketchar. The paper should clarify exactly what the baseline condition was and temper causal phrasing accordingly.
  4. [§4.4] Factor-level claims about the Collaboration dimension are asserted without reported statistical detail. The text says a Tukey test showed Collaboration scores higher than other dimensions but then states 'We did not statistically test each factor score. Instead, we compare them directly.' No test statistics, adjusted p-values, or effect sizes are given for the individual CSI dimensions. Given that the Collaboration subscale is the load-bearing evidence for the paper's main claim, the authors should either report the supporting statistics or explicitly frame the dimension comparisons as descriptive.
minor comments (3)
  1. [§4.4] The final paragraph of §4.4 uses 'Sketcher' instead of 'Sketchar' in the sentence about the exploration dimension; please correct the spelling for consistency.
  2. [Figure 8 caption] The caption says the displayed results involve 'six experts (P1, P2, P3, P9, P10, and P11),' but P1-P13 are identifiers from the qualitative user study, not the expert evaluators (E1-E5). Clarify whether these are user-study participants or illustrators.
  3. [Table 2] The experience categories in Table 2 are ambiguous: 'No more than 3 years' and 'No less than 1 year' overlap and do not partition the sample clearly. Use mutually exclusive ranges such as 'less than 1 year,' '1-3 years,' and 'more than 3 years.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claims rest on a randomized comparative user study and independent expert evaluation, not on fitted parameters or self-cited derivation.

full rationale

This is an empirical systems paper, not a derivation. The central quantitative claim (Sketchar yields significantly higher CSI than a sketching-only baseline, p = .028; §4.4) is a between-condition comparison against an external baseline, so it cannot reduce by construction to the tool's inputs. The non-artist subgroup result (p = .011) is likewise a measured difference, not a fitted prediction. The qualitative findings (KF1-KF3) and the expert evaluation (§4.5), in which five illustrators independently judged generated images against their own workflows, provide external evidence for the workflow-integration claim. The authors' self-citations ([26], [43], [69], [77]) appear only in related-work context and are not load-bearing for the reported results. The paper itself flags the main validity limitation in §5.5: the CSI Collaboration subscale is designed for synchronous collaboration, whereas Sketchar was tested with individuals imagining an illustrator handoff, so 'the perception that the tool supports collaboration may be speculative.' That is an honest admission of construct validity limits, not a circular reduction: the Collaboration score was not defined in terms of the conclusion, and the imagined-handoff result was not used to fit any parameter. Design goals derived from formative interviews could bias what was measured, but the outcome measures (CSI, interviews, expert ratings) were not equivalent to those goals by construction. No equation, fitted parameter, or uniqueness theorem is invoked to force the conclusions, so there is no circular step to report.

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

The tool and study rest on domain assumptions about how user studies measure collaboration and skill; no numeric free parameters are fitted to data, and no new theoretical entities are introduced.

assumptions (4)
  • domain assumption Imagined designer-illustrator collaboration in the study approximates real collaboration.
    Section 5.5 states that single individuals worked with Sketchar and collaboration 'had to be imagined'; all collaboration-related CSI results depend on this assumption.
  • domain assumption The CSI collaboration subscale is valid for asynchronous, imagined collaboration.
    Sections 4.2 and 5.5 note that the subscale normally refers to synchronous collaboration and that the perception of collaboration 'may be speculative.'
  • domain assumption Self-reported artistic skill groups are meaningful.
    Both studies divide participants into art and no-art groups based on self-report without an objective skill test; this grouping drives the main subgroup finding.
  • domain assumption Third-party model outputs (GPT-3.5, DALL-E 2) are representative of what Sketchar would produce.
    Sketchar's behavior depends on external models that change over time; the paper proposes switching models in Section 5.5, so results may not generalize to other generators.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI." pith.science (2026). https://pith.science/paper/NHUR5WV5

@misc{pith2026250812333,
  author       = {Pith},
  title        = {Pith review of: Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NHUR5WV5}},
  note         = {Machine review of arXiv:2508.12333}
}
read the original abstract

Character design in games involves interdisciplinary collaborations, typically between designers who create the narrative content, and illustrators who realize the design vision. However, traditional workflows face challenges in communication due to the differing backgrounds of illustrators and designers, the latter with limited artistic abilities. To overcome these challenges, we created Sketchar, a Generative AI (GenAI) tool that allows designers to prototype game characters and generate images based on conceptual input, providing visual outcomes that can give immediate feedback and enhance communication with illustrators' next step in the design cycle. We conducted a mixed-method study to evaluate the interaction between game designers and Sketchar. We showed that the reference images generated in co-creating with Sketchar fostered refinement of design details and can be incorporated into real-world workflows. Moreover, designers without artistic backgrounds found the Sketchar workflow to be more expressive and worthwhile. This research demonstrates the potential of GenAI in enhancing interdisciplinary collaboration in the game industry, enabling designers to interact beyond their own limited expertise.

Figures

Figures reproduced from arXiv: 2508.12333 by the authors.

Figure 1
Figure 1. Sketchar is a creative tool for game designers to generate game character profiles using natural [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Interview Flowchart: We conducted interviews with both designers and illustrators to elucidate the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3.1
Figure 3.1. She can specify key traits, attributes, or contextual details related to the character’s [PITH_FULL_IMAGE:figures/full_fig_p008_3_1.png] view at source ↗
Figures from the paper (13 more)
Figure 5
Figure 5. Figure 5: Hierarchical content generation means that the content is generated through a structured, [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 3
Figure 3. Figure 3: Sketchar walkthrough. Step 1: Initialization - Users input their design intentions in the initial state. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png]
Figure 4
Figure 4. Figure 4: System Architecture: This system is devised to integrate robust artificial intelligence models(DALLE and GPT-3.5) into a web-based framework with CLIP and prompt engineering. The communication parts are represented by dashed lines, while solid lines introduce the adopt…
Figure 5
Figure 5. Figure 5: Prompt Process Flow: (1) Phases: In the generation phase, character profiles will be obtained in the order of input, summary, and generation. In the interaction phase, users can chat with the agent and build a family tree. (2) Examples: Presenting certain prompts along…
Figure 6
Figure 6. Figure 6: Simulate Chat Workflow: Step 1, Enter the chat contents. Step 2, Receive a personalized answer. Step 3, Repeat the process. Family Tree Workflow: Step 1, Utilize the ability of React Flow Module to build relationships by dragging lines. Step 2, Fill in the relationship…
Figure 7
Figure 7. Figure 7: Qualitative Study Pipeline: (a) Commence the experiment by introducing it and obtaining informed consent from participants. (b) Allocate 20 minutes for the completion of Task 1. (c) Provide instructions for Sketchar and allow 25 minutes for the completion of Task 2. (d…
Figure 8
Figure 8. Figure 8: In the interviews, the interviewees received Task 2: Generating the protagonist for a fresh platform [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: A study process example of interviewee P6: Step 1 involves preliminary planning and brainstorming. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 11
Figure 11. Figure 11: Correspondence of the image and prompt words: Taking participant P5’s results as an example, and mapping features of the outcome image to the original inputs. KF2: Classifying and expanding character design concepts through GenAI assists game designers in expressing c…
Figure 12
Figure 12. Figure 12: The comparative analysis of the total CSI scores and factor scores. (a) The average CSI score for [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Example reference images generated by participants in the study. (a) Prompt 1: A very cool boy with [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Generated by DALLE, Prompt: A brave male from the Maori community in New Zealand, Our study suggested that AI tools support creative collaboration and communication in game development. However, they cannot replace human input due to issues like lighting, character po…
Figure 15
Figure 15. Figure 15: Six dimensions and specific descriptions of CSI. The answer for each item is "High Disagree" (0) to [PITH_FULL_IMAGE:figures/full_fig_p028_15.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

77 extracted references · 55 canonical work pages

  1. [1]

    [n. d.]. Lexica. https://lexica.art/

    work page

  2. [2]

    AlgorithmWatch. 2024. Image Generators Reinforce Stereotypes, Lack Diversity: A Study by AlgorithmWatch. AlgorithmWatch (2024). https://algorithmwatch.org/en/image-generators-stereotypes-diversity/ Accessed: 2024-05- 26

    work page 2024

  3. [3]

    Janna Anderson and Lee Rainie. 2023. As AI spreads, experts predict the best and worst changes in digital life by 2035. (2023)

    work page 2023

  4. [4]

    Tyler Angert, Miroslav Ivan Suzara, Jenny Han, Christopher Lawrence Pondoc, and Hariharan Subramonyam. 2023. Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts. arXiv preprint arXiv:2308.03921 (2023)

    work page Pith review arXiv 2023

  5. [5]

    Rahul Arora, Ishan Darolia, Vinay P Namboodiri, Karan Singh, and Adrien Bousseau. 2017. Sketchsoup: Exploratory ideation using design sketches. In Computer Graphics Forum, Vol. 36. Wiley Online Library, 302–312

    work page 2017

  6. [6]

    Bloomberg. 2023. Generative AI and Bias: A Deep Dive by Bloomberg. Bloomberg (2023). https://www.bloomberg. com/graphics/2023-generative-ai-bias/ Accessed: 2024-05-26

    work page 2023

  7. [7]

    Ali Borji. 2023. Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2. arXiv:2210.00586 [cs.CV]

    arXiv 2023

  8. [8]

    Josiah D Boucher, Gillian Smith, and Yunus Doğan Telliel. 2024. Is Resistance Futile?: Early Career Game Developers, Generative AI, and Ethical Skepticism. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–13

    work page 2024

Show all 77 references
  1. [9]

    Gwern Branwen. 2020. GPT-3 creative fiction. (2020)

    2020

  2. [10]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101

    2006

  3. [11]

    Alice Cai, Steven R Rick, Jennifer L Heyman, Yanxia Zhang, Alexandre Filipowicz, Matthew Hong, Matt Klenk, and Thomas Malone. 2023. DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration. In Proceedings of The ACM Collective Intelligence Conference . 1–11

    2023

  4. [12]

    Cassiano Canheti, Flávio Andalo, and Milton Luiz Horn Vieira. 2019. Case study: Game character creation process. In Advances in Human Factors in Wearable Technologies and Game Design: Proceedings of the AHFE 2018 International Conferences on Human Factors and Wearable Technolo...

    2019

  5. [13]

    Erin Cherry and Celine Latulipe. 2014. Quantifying the creativity support of digital tools through the creativity support index. ACM Transactions on Computer-Human Interaction (TOCHI) 21, 4 (2014), 1–25

    2014

  6. [14]

    John Joon Young Chung. 2022. Artistic user expressions in AI-powered creativity support tools. In Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology . 1–4

    2022

  7. [15]

    John Joon Young Chung and Eytan Adar. 2023. Artinter: AI-powered Boundary Objects for Commissioning Visual Arts. (2023)

    2023

  8. [16]

    John Joon Young Chung, Shiqing He, and Eytan Adar. 2022. Artist support networks: Implications for future creativity support tools. In Designing Interactive Systems Conference . 232–246. Proc. ACM Hum.-Comput. Interact., Vol. 8, No. CHI PLAY, Article 337. Publication date: Oct...

    2022

  9. [17]

    John Joon Young Chung, Wooseok Kim, Kang Min Yoo, Hwaran Lee, and Eytan Adar. [n. d.]. TaleBrush: Sketching Stories with Generative Pretrained Language Models. In CHI Conference on Human Factors in Computing Systems (New Orleans LA USA, 2022-04-29). ACM, 1–19. https://doi.org/...

    2022

  10. [18]

    Niklas Deckers, Maik Fröbe, Johannes Kiesel, Gianluca Pandolfo, Christopher Schröder, Benno Stein, and Martin Potthast. 2023. The Infinite Index: Information Retrieval on Generative Text-To-Image Models. In Proceedings of the 2023 Conference on Human Information Interaction an...

    2023

  11. [19]

    Ferda Gül Aydın Emekligil and İlkay Öksüz. 2022. Game Character Generation with Generative Adversarial Networks. In 2022 30th Signal Processing and Communications Applications Conference (SIU). 1–4. https://doi.org/10.1109/SIU55565. 2022.9864747

    2022

  12. [20]

    Peter Fernandez. 2022. Technology behind text to image generators. Library Hi Tech News 39, 10 (2022), 1–4

    2022

  13. [21]

    Elise Fogelström. 2013. Investigation of shapes and colours as elements of character design

    2013

  14. [22]

    Forbes Middle East. 2023. ChatGPT Reportedly Blocked on Chinese Social Media Apps as Beijing Claims AI is Used to Spread Propaganda. https://www.forbesmiddleeast.com/innovation/artificial-intelligence-machine-learning/chatgpt- reportedly-blocked-on-chinese-social-media-appsas-...

    2023

  15. [23]

    Fiona Fui-Hoon Nah, Ruilin Zheng, Jingyuan Cai, Keng Siau, and Langtao Chen. 2023. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. , 277–304 pages

    2023

  16. [24]

    Lilian Guo and Anand Bhojan. 2022. Converting Nebulous Ideas to Reality–A Deep Learning Tool for Conditional Synthesis of Character Designs. In International Conference on Computer, Communication, and Signal Processing . Springer, 75–89

    2022

  17. [25]

    Ariel Han and Zhenyao Cai. 2023. Design implications of generative AI systems for visual storytelling for young learners. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference . 470–474

    2023

  18. [26]

    Yuanning Han, Ziyi Qiu, Jiale Cheng, and RAY LC. 2024. When Teams Embrace AI: Human Collaboration Strategies in Generative Prompting in a Creative Design Task. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–14

    2024

  19. [27]

    Seunghoon Hong, Dingdong Yang, Jongwook Choi, and Honglak Lee. 2018. Inferring semantic layout for hierarchical text-to-image synthesis. In Proceedings of the IEEE conference on computer vision and pattern recognition . 7986–7994

    2018

  20. [28]

    John Howard. 2019. Artificial intelligence: Implications for the future of work. American journal of industrial medicine 62, 11 (2019), 917–926

    2019

  21. [29]

    Forrest Huang and John F Canny. 2019. Sketchforme: Composing sketched scenes from text descriptions for interactive applications. In Proceedings of the 32nd annual ACM symposium on user interface software and technology . 209–220

    2019

  22. [30]

    Forrest Huang, Eldon Schoop, David Ha, and John Canny. 2020. Scones: towards conversational authoring of sketches. In Proceedings of the 25th International Conference on Intelligent User Interfaces . 313–323

    2020

  23. [31]

    Angel Hsing-Chi Hwang. [n. d.]. Too Late to be Creative? AI-Empowered Tools in Creative Processes. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2022-04-28) (CHI EA ’22). Association for Computing Machinery, 1–9. http...

    2022

  24. [32]

    Nanna Inie, Jeanette Falk, and Steve Tanimoto. 2023. Designing participatory ai: Creative professionals’ worries and expectations about generative ai. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–8

    2023

  25. [33]

    Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam. 2022. Creative writing with an ai-powered writing assistant: Perspectives from professional writers. arXiv preprint arXiv:2211.05030 (2022)

    2022 arXiv

  26. [34]

    Shih, and Kyungsik Han

    Youngseung Jeon, Seungwan Jin, Patrick C. Shih, and Kyungsik Han. [n. d.]. FashionQ: An AI-Driven Creativity Support Tool for Facilitating Ideation in Fashion Design. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2021-05-07...

    2021

  27. [35]

    Hyung-Kwon Ko, Subin An, Gwanmo Park, Seung Kwon Kim, Daesik Kim, Bohyoung Kim, Jaemin Jo, and Jinwook Seo. 2022. We-toon: A Communication Support System between Writers and Artists in Collaborative Webtoon Sketch Revision. In Proceedings of the 35th Annual ACM Symposium on Us...

    2022

  28. [36]

    Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo, Juho Kim, and Jinwook Seo. 2023. Large-scale text-to-image generation models for visual artists’ creative works. In Proceedings of the 28th International Conference on Intelligent User Interfaces. 919–933

    2023

  29. [37]

    Marianne Krawczyk and Jeannie Novak. 2006. Game development essentials: Game story & character development. (No Title) (2006)

    2006

  30. [38]

    Max Kreminski and Chris Martens. 2022. Unmet creativity support needs in computationally supported creative writing. In Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022) . 74–82

    2022

  31. [39]

    Hagung Kuntjara, Betha Almanfaluthi, et al. 2017. Character design in games analysis of character design theory. Journal of Games, Game Art, and Gamification 2, 2 (2017). Proc. ACM Hum.-Comput. Interact., Vol. 8, No. CHI PLAY, Article 337. Publication date: October 2024. 337:2...

    2017

  32. [40]

    Elisa Kwon, Vivek Rao, and Kosa Goucher-Lambert. 2023. Understanding inspiration: Insights into how designers discover inspirational stimuli using an AI-enabled platform. Design Studies 88 (2023), 101202

    2023

  33. [41]

    Petri Lankoski. 2004. Character design fundamentals for role-playing games. Beyond Role and Play (2004), 139–148

    2004

  34. [42]

    Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser. 2017. Research methods in human-computer interaction . Morgan Kaufmann

    2017

  35. [43]

    RAY LC and Yuying Tang. 2023. Speculative Design with Generative AI: Applying Stable Diffusion and ChatGPT to imagining climate change futures. In Proceedings of the 11th International Conference on Digital and Interactive Arts . 1–8

    2023

  36. [44]

    Josiah Lebowitz and Chris Klug. 2011. Interactive storytelling for video games: A player-centered approach to creating memorable characters and stories . Taylor & Francis

    2011

  37. [45]

    Sola Lee, Suin Jun, Mihye Park, Sui Park, and Jongin Choi. 2021. Character Generation and Storytelling using Modeling Tool and Game Engine. In Proceedings of the Korean Institute of Information and Commucation Sciences Conference . The Korea Institute of Information and Commuc...

    2021

  38. [46]

    Jie Li, Hancheng Cao, Laura Lin, Youyang Hou, Ruihao Zhu, and Abdallah El Ali. 2024. User experience design professionals’ perceptions of generative artificial intelligence. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–18

    2024

  39. [47]

    Eliott Lilly. 2017. The Big Bad World of Concept Art for Video Games: How to Start Your Career as a Concept Artist . Design Studio Press

    2017

  40. [48]

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1–35

    2023

  41. [49]

    Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2015. Generating images from captions with attention. arXiv preprint arXiv:1511.02793 (2015)

    2015 arXiv

  42. [50]

    Gary Marcus, Ernest Davis, and Scott Aaronson. 2022. A very preliminary analysis of DALL-E 2. arXiv preprint arXiv:2204.13807 (2022)

    2022 arXiv

  43. [51]

    Margaret Mark and Carol S Pearson. 2001. O herói e o fora-da-lei: como construir marcas extraordinárias usando o poder dos arquétipos. São Paulo: Pensamento (2001)

    2001

  44. [52]

    Dalila Martins, Leonardo Pereira, Ana Patrícia Oliveira, and Nelson Zagalo. 2021. Character Design: The Case of Characters in a Hybrid Serious Game Called FlavourGame. In International Conference on Design and Digital Communication. Springer, 183–194

    2021

  45. [53]

    Piotr Mirowski, Kory W Mathewson, Jaylen Pittman, and Richard Evans. 2023. Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–34

    2023

  46. [54]

    Michael Muller, Lydia B Chilton, Anna Kantosalo, Charles Patrick Martin, and Greg Walsh. [n. d.]. GenAICHI: Generative AI and HCI. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (New Orleans LA USA, 2022-04-27). ACM, 1–7. https://doi.org/10.1145/349...

    2022

  47. [55]

    John Naughton. 2023. The ChatGPT bot is causing panic now–but it’ll soon be as mundane a tool as Excel. The Guardian 7 (2023)

    2023

  48. [56]

    Kongdee Nop, Jintapitak Manissaward, and Tangto Oattarapon. 2019. Development of character design frameworks using game engine: unreal engine. In 2019 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, elec...

    2019

  49. [57]

    Changhoon Oh, Jungwoo Song, Jinhan Choi, Seonghyeon Kim, Sungwoo Lee, and Bongwon Suh. 2018. I lead, you help but only with enough details: Understanding user experience of co-creation with artificial intelligence. In Proceedings of the 2018 CHI Conference on Human Factors in ...

    2018

  50. [58]

    Jonas Oppenlaender. 2022. A taxonomy of prompt modifiers for text-to-image generation. arXiv preprint arXiv:2204.13988 2 (2022)

    2022 arXiv

  51. [59]

    Hua Xuan Qin, Shan Jin, Ze Gao, Mingming Fan, and Pan Hui. 2024. CharacterMeet: Supporting Creative Writers’ Entire Story Character Construction Processes Through Conversation with LLM-Powered Chatbot Avatars. In Proceedings of the CHI Conference on Human Factors in Computing ...

    2024

  52. [60]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learni...

    2021

  53. [61]

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. [n. d.]. Zero-Shot Text-to-Image Generation. In Proceedings of the 38th International Conference on Machine Learning (2021-07-01). PMLR, 8821–8831. https://procee...

    2021

  54. [62]

    Oliver Schmitt and Daniel Buschek. 2021. Characterchat: Supporting the creation of fictional characters through conversation and progressive manifestation with a chatbot. In Creativity and Cognition. 1–10

    2021

  55. [63]

    Tianyang Shi, Zhengxia Zou, Xinhui Song, Zheng Song, Changjian Gu, Changjie Fan, and Yi Yuan. 2020. Neutral face game character auto-creation via pokerface-gan. In Proceedings of the 28th ACM International Conference on Multimedia . 3201–3209

    2020

  56. [64]

    Chris Solarski. 2012. Drawing basics and video game art: Classic to cutting-edge art techniques for winning video game design. Watson-Guptill

    2012

  57. [65]

    Angie Spoto, Natalia Oleynik, Sebastian Deterding, and Jon Hook. 2017. Library of Mixed-Initiative Creative Interfaces

    2017

  58. [66]

    Luke Stark and Kate Crawford. 2019. The work of art in the age of artificial intelligence: What artists can teach us about the ethics of data practice. Surveillance & Society 17, 3/4 (2019), 442–455

    2019

  59. [67]

    Sangho Suh, Jian Zhao, and Edith Law. 2022. Codetoon: Story ideation, auto comic generation, and structure mapping for code-driven storytelling. InProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–16

    2022

  60. [68]

    Qirui Sun, Qiaoyang Luo, Yunyi Ni, and Haipeng Mi. 2024. Text2AC: A Framework for Game-Ready 2D Agent Character (AC) Generation from Natural Language. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–7

    2024

  61. [69]

    Yuqian Sun, Xuran Ni, Haozhen Feng, Ray LC, Chang Hee Lee, and Ali Asadipour. 2022. Bringing stories to life in 1001 nights: A co-creative text adventure game using a story generation model. In International Conference on Interactive Digital Storytelling. Springer, 651–672

    2022

  62. [70]

    P Tavares and PM Teixeira. 2011. Drawing and project: tradition and innovation. InProceedings of Conferência Designa

    2011

  63. [71]

    Mathias Peter Verheijden and Mathias Funk. 2023. Collaborative Diffusion: Boosting Designerly Co-Creation with Generative AI. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems . 1–8

    2023

  64. [72]

    Jesse Voimala. 2023. Creating a Video Game Character Design Workflow: a literature review on the theories and methods used by professionals in video game character design. (2023)

    2023

  65. [73]

    Xingzhi Wang, Nabil Anwer, Yun Dai, and Ang Liu. 2023. ChatGPT for design, manufacturing, and education. Procedia CIRP 119 (2023), 7–14

    2023

  66. [74]

    Daijin Yang, Yanpeng Zhou, Zhiyuan Zhang, and Toby Jia-Jun Li. [n. d.]. AI as an Active Writer: Interaction strategies with generated text in human-AI collaborative fiction writing. ([n. d.])

  67. [75]

    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas. 2017. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision ....

    2017

  68. [76]

    Zheng Zhang, Jie Gao, Ranjodh Singh Dhaliwal, and Toby Jia-Jun Li. 2023. VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping. arXiv preprint arXiv:2304.07810 (2023)

    2023 arXiv

  69. [77]

    High Disagree

    Suifang Zhou, Latisha Besariani Hendra, Qinshi Zhang, Jussi Holopainen, and RAY LC. 2024. Eternagram: Probing Player Attitudes Towards Climate Change Using a ChatGPT-driven Text-based Adventure. InProceedings of the CHI Conference on Human Factors in Computing Systems . 1–23. ...

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.