Keyframer: Empowering Animation Design using Large Language Models
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.
65c981c6fdfcd887da71b7b1
Tiffany Tseng
admin_assigned
2024-02-12T16:53:24.031Z
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Tiffany Tseng
user
scientiffic
65c981c6fdfcd887da71b7b2
Ruijia Cheng
65c981c6fdfcd887da71b7b3
Jeffrey Nichols
Keyframer: Empowering Animation Design using Large Language Models
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.
Keyframer, a natural language-driven animation design tool, allows users to create and refine SVG animations through prompting and direct editing, informed by professional insights and user studies.
LLMs Keyframer SVGs prompting direct editing user prompting strategies semantic prompt types decomposed prompting style