关键帧生成器：利用大型语言模型赋能动画设计

摘要

大型语言模型（LLMs）有潜力影响广泛的创意领域，但将LLMs应用于动画尚未得到充分探讨，并提出了新的挑战，例如用户如何有效地用自然语言描述运动。在本文中，我们提出了Keyframer，这是一个用自然语言为静态图像（SVGs）制作动画的设计工具。Keyframer受专业动画设计师和工程师的访谈启发，通过提示和直接编辑生成的输出的结合，支持动画的探索和完善。该系统还使用户能够请求设计变体，支持比较和构思。通过与13名参与者进行的用户研究，我们提出了用户提示策略的表征，包括用于描述运动的语义提示类型的分类法以及一种“分解”提示风格，用户不断根据生成的输出调整其目标。我们分享了直接编辑和提示如何使得用户能够在当今生成工具中常见的一次性提示界面之外进行迭代。通过这项工作，我们提出了LLMs如何赋予各种受众参与动画创作的可能性。

English

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.

关键帧生成器：利用大型语言模型赋能动画设计

Keyframer: Empowering Animation Design using Large Language Models

摘要

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