LiveSVG fits original SVG paths to a generated target video via dual-level motion (group homographies plus per-path Bezier offsets) and sphere-packing recolorization to achieve zero-shot, skeleton-free SVG animation.
Keyframer: Empowering animation design using large language models
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language prompts, enabling users to preview rendered animations inline and edit them directly through provided editors. Through a user study with 13 novices and experts in animation design and programming, we contribute 1) a categorization of semantic prompt types for describing motion and identification of a 'decomposed' prompting style where users continually adapt their goals in response to generated output; and 2) design insights on supporting iterative refinement of animations through the combination of direct editing and natural language interfaces.
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VAnim creates open-domain text-to-SVG animations via sparse state updates on a persistent DOM tree, identification-first planning, and rendering-aware RL with a new 134k-example benchmark.
LottieGPT tokenizes Lottie animations into compact sequences and fine-tunes Qwen-VL to autoregressively generate coherent vector animations from natural language or visual prompts, outperforming prior SVG models.
fog composes LLM-generated motion functions (verbs/adverbs/gestures/emotions) so people recognize intended semantics in Heider-Simmel animations at 68% accuracy and iterate faster than pure prompting.
An iterative design of Flowcode shows that LLM-generated flowcharts plus step-by-step fill-in-the-blank explanations help novices orient, navigate, and extend found creative-coding projects.
DataSway supports creation of semantically aligned animations for metaphoric data visualizations by generating clips via VLMs and coordinating timelines based on entity order, attributes, layout, or randomness.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
citing papers explorer
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LiveSVG: Zero-Shot SVG Animation via Video Generation
LiveSVG fits original SVG paths to a generated target video via dual-level motion (group homographies plus per-path Bezier offsets) and sphere-packing recolorization to achieve zero-shot, skeleton-free SVG animation.
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VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
VAnim creates open-domain text-to-SVG animations via sparse state updates on a persistent DOM tree, identification-first planning, and rendering-aware RL with a new 134k-example benchmark.
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LottieGPT: Tokenizing Vector Animation for Autoregressive Generation
LottieGPT tokenizes Lottie animations into compact sequences and fine-tunes Qwen-VL to autoregressively generate coherent vector animations from natural language or visual prompts, outperforming prior SVG models.
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fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code
fog composes LLM-generated motion functions (verbs/adverbs/gestures/emotions) so people recognize intended semantics in Heider-Simmel animations at 68% accuracy and iterate faster than pure prompting.
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Flowcode: An AI-Powered Programming Environment for Scaffolding Iteration in Creative Computing Education
An iterative design of Flowcode shows that LLM-generated flowcharts plus step-by-step fill-in-the-blank explanations help novices orient, navigate, and extend found creative-coding projects.
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DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination
DataSway supports creation of semantically aligned animations for metaphoric data visualizations by generating clips via VLMs and coordinating timelines based on entity order, attributes, layout, or randomness.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.