title: Keyframer: Empowering Animation Design using Large Language Models
summary: Large language models (LLMs) have the potential to impact a wide range of
  creative domains, but the application of LLMs to animation is underexplored and
  presents novel challenges such as how users might effectively describe motion
  in natural language. In this paper, we present Keyframer, a design tool for
  animating static images (SVGs) with natural language. Informed by interviews
  with professional animation designers and engineers, Keyframer supports
  exploration and refinement of animations through the combination of prompting
  and direct editing of generated output. The system also enables users to
  request design variants, supporting comparison and ideation. Through a user
  study with 13 participants, we contribute a characterization of user prompting
  strategies, including a taxonomy of semantic prompt types for describing motion
  and a 'decomposed' prompting style where users continually adapt their goals in
  response to generated output.We share how direct editing along with prompting
  enables iteration beyond one-shot prompting interfaces common in generative
  tools today. Through this work, we propose how LLMs might empower a range of
  audiences to engage with animation creation.
paper_authors._id: 65c981c6fdfcd887da71b7b1
paper_authors.name: Tiffany Tseng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-12T16:53:24.031Z
paper_authors._id: 631a1333fac58c9c815f4d6a
paper_authors.avatarUrl: /avatars/0c7a3d1f4d51d4fb5f776238714e6498.svg
paper_authors.fullname: Tiffany Tseng
paper_authors.type: user
paper_authors.user: scientiffic
paper_authors._id: 65c981c6fdfcd887da71b7b2
paper_authors.name: Ruijia Cheng
paper_authors._id: 65c981c6fdfcd887da71b7b3
paper_authors.name: Jeffrey Nichols
paper_title: Keyframer: Empowering Animation Design using Large Language Models
paper_summary: Large language models (LLMs) have the potential to impact a wide range of
  creative domains, but the application of LLMs to animation is underexplored and
  presents novel challenges such as how users might effectively describe motion
  in natural language. In this paper, we present Keyframer, a design tool for
  animating static images (SVGs) with natural language. Informed by interviews
  with professional animation designers and engineers, Keyframer supports
  exploration and refinement of animations through the combination of prompting
  and direct editing of generated output. The system also enables users to
  request design variants, supporting comparison and ideation. Through a user
  study with 13 participants, we contribute a characterization of user prompting
  strategies, including a taxonomy of semantic prompt types for describing motion
  and a 'decomposed' prompting style where users continually adapt their goals in
  response to generated output.We share how direct editing along with prompting
  enables iteration beyond one-shot prompting interfaces common in generative
  tools today. Through this work, we propose how LLMs might empower a range of
  audiences to engage with animation creation.
paper_ai_keywords: large language models (LLMs) design tool animations SVGs natural language prompting direct editing design variants user prompting strategies semantic prompt types 'decomposed' prompting style one-shot prompting interfaces generative tools