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.

翻译：大型语言模型（LLMs）有潜力影响广泛的创意领域，但LLMs在动画领域的应用尚待深入探索，并带来了新颖挑战，例如用户如何有效通过自然语言描述运动。本文介绍了Keyframer，一个支持通过自然语言为静态图像（SVG）添加动画的设计工具。基于对专业动画设计师和工程师的访谈，Keyframer结合了提示生成与对生成输出的直接编辑，支持动画的探索与精炼。该系统还允许用户请求设计变体，支持对比与构思。通过一项包含13名参与者的用户研究，我们贡献了对用户提示策略的特征化描述，包括描述运动的语义提示类型分类法，以及一种“分解式”提示风格——用户根据生成输出不断调整自身目标。我们揭示了直接编辑与提示相结合如何超越了当前生成式工具中常见的一次性提示界面，推动了迭代过程。通过本研究，我们提出了LLMs可如何赋能不同受众参与动画创作。