Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts
Tyler Angert, Miroslav Ivan Suzara, Jenny Han, Christopher Lawrence Pondoc, Hariharan Subramonyam
摘要
Creative coding tasks are often exploratory in nature. When producing digital artwork, artists usually begin with a high-level semantic construct such as a “stained glass filter” and programmatically implement it by varying code parameters such as shape, color, lines, and opacity to produce visually appealing results. Based on interviews with artists, it can be effortful to translate semantic constructs to program syntax, and current programming tools don’t lend well to rapid creative exploration. To address these challenges, we introduce Spellburst, a large language model (LLM) powered creative-coding environment. Spellburst provides (1) a node-based interface that allows artists to create generative art and explore variations through branching and merging operations, (2) expressive prompt-based interactions to engage in semantic programming, and (3) dynamic prompt-driven interfaces and direct code editing to seamlessly switch between semantic and syntactic exploration. Our evaluation with artists demonstrates Spellburst’s potential to enhance creative coding practices and inform the design of computational creativity tools that bridge semantic and syntactic spaces.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper29
- Bridging the Gulf of Envisioning: Cognitive Challenges in Prompt Based Interactions with LLMsHariharan Subramonyam, Roy Pea, Christopher Lawrence Pondoc, Maneesh Agrawala 等CHI 2024 · 被引用 137 次
- DirectGPT: A Direct Manipulation Interface to Interact with Large Language ModelsDamien Masson, Sylvain Malacria, Géry Casiez, Daniel VogelCHI 2024 · 被引用 104 次
- WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code VisualizationLiwenhan Xie, Chengbo Zheng, Haijun Xia, Huamin Qu 等UIST 2024 · 被引用 45 次
- Misty: UI Prototyping Through Interactive Conceptual BlendingYuwen Lu, Alan Leung, Amanda Swearngin, Jeffrey Nichols 等CHI 2025 · 被引用 41 次
- KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationYoufu Yan, Yu Hou, Yongkang Xiao, Rui Zhang 等IEEE VIS 2024 · 被引用 33 次
它引用的顶会 Paper13
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Design Guidelines for Prompt Engineering Text-to-Image Generative ModelsVivian Liu, Lydia B. ChiltonCHI 2022 · 被引用 586 次
- An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual InversionRinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik 等ICLR 2023 · 被引用 464 次
- Grounded Copilot: How Programmers Interact with Code-Generating ModelsShraddha Barke, Michael B. James, Nadia PolikarpovaOOPSLA 2023 · 被引用 408 次
相关 Paper
- DynEx: Dynamic Code Synthesis with Structured Design Exploration for Accelerated Exploratory ProgrammingJenny Guangzhen Ma, Karthik Sreedhar, Vivian Liu, Pedro Alejandro Perez 等CHI 2025 · 被引用 11 次
- Discovering the Syntax and Strategies of Natural Language Programming with Generative Language ModelsEllen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson 等CHI 2022 · 被引用 76 次
- Casting a SPELL: Sentence Pairing Exploration for LLM Limitation-BreakingYifan Huang, Xiaojun Jia, Wenbo Guo, Yuqiang Sun 等FSE 2026
- Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-CreationSangho Suh, Meng Chen, Bryan Min, Toby Jia-Jun Li 等CHI 2024 · 被引用 143 次
- Why Johnny Can't Prompt: How Non-AI Experts Try (and Fail) to Design LLM PromptsJ. D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qian YangCHI 2023 · 被引用 892 次
