Keyframer: Empowering Animation Design using Large Language Models
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916894222581760 |
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| author | Tseng, Tiffany Cheng, Ruijia Nichols, Jeffrey |
| author_facet | Tseng, Tiffany Cheng, Ruijia Nichols, Jeffrey |
| contents | Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language prompts, enabling users to preview rendered animations inline and edit them directly through provided editors. Through a user study with 13 novices and experts in animation design and programming, we contribute 1) a categorization of semantic prompt types for describing motion and identification of a 'decomposed' prompting style where users continually adapt their goals in response to generated output; and 2) design insights on supporting iterative refinement of animations through the combination of direct editing and natural language interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Keyframer: Empowering Animation Design using Large Language Models Tseng, Tiffany Cheng, Ruijia Nichols, Jeffrey Human-Computer Interaction Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language prompts, enabling users to preview rendered animations inline and edit them directly through provided editors. Through a user study with 13 novices and experts in animation design and programming, we contribute 1) a categorization of semantic prompt types for describing motion and identification of a 'decomposed' prompting style where users continually adapt their goals in response to generated output; and 2) design insights on supporting iterative refinement of animations through the combination of direct editing and natural language interfaces. |
| title | Keyframer: Empowering Animation Design using Large Language Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.06071 |