關鍵幀生成器：利用大型語言模型增強動畫設計

摘要

大型語言模型（LLMs）有潛力影響廣泛的創意領域，但將LLMs應用於動畫領域尚未深入研究，並提出了新挑戰，例如用戶如何有效地用自然語言描述運動。本文介紹了一種名為Keyframer的設計工具，用於通過自然語言為靜態圖像（SVGs）添加動畫。Keyframer根據對專業動畫設計師和工程師的訪談，通過提示和直接編輯生成的輸出的組合，支持動畫的探索和完善。該系統還使用戶能夠請求設計變體，支持比較和構思。通過對13名參與者進行的用戶研究，我們提出了用戶提示策略的特徵，包括用於描述運動的語義提示類型的分類法以及一種“分解”的提示風格，其中用戶不斷根據生成的輸出調整其目標。我們分享了直接編輯以及提示如何使得在當今生成工具中常見的一次性提示界面之外進行迭代。通過這項工作，我們提出了LLMs如何賦予各種受眾參與動畫創作的可能性。

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.

關鍵幀生成器：利用大型語言模型增強動畫設計

Keyframer: Empowering Animation Design using Large Language Models

摘要

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