OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens
This addresses the challenge of creating vector animations from text and visual inputs for designers and developers, though it appears incremental as it builds on existing vision-language models.
The authors tackled the problem of generating vector animations from multi-modal instructions by introducing OmniLottie, a framework that uses a novel Lottie tokenizer to convert JSON files into structured sequences, enabling high-quality animation generation with semantic alignment to instructions.
OmniLottie is a versatile framework that generates high quality vector animations from multi-modal instructions. For flexible motion and visual content control, we focus on Lottie, a light weight JSON formatting for both shapes and animation behaviors representation. However, the raw Lottie JSON files contain extensive invariant structural metadata and formatting tokens, posing significant challenges for learning vector animation generation. Therefore, we introduce a well designed Lottie tokenizer that transforms JSON files into structured sequences of commands and parameters representing shapes, animation functions and control parameters. Such tokenizer enables us to build OmniLottie upon pretrained vision language models to follow multi-modal interleaved instructions and generate high quality vector animations. To further advance research in vector animation generation, we curate MMLottie-2M, a large scale dataset of professionally designed vector animations paired with textual and visual annotations. With extensive experiments, we validate that OmniLottie can produce vivid and semantically aligned vector animations that adhere closely to multi modal human instructions.