OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens
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.
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Yiying Yang
claimed_verified
2026-03-03T10:38:28.746Z
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yangyiying
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user
yangyangyiying
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Wei Cheng
claimed_verified
2026-03-03T10:38:31.631Z
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Wei Cheng
wchengad
user
wchengad
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Sijin Chen
claimed_verified
2026-03-03T10:38:26.267Z
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sijin
CH3COOK
user
CH3COOK
69a6699b663a122a7ce667a3
Honghao Fu
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Xianfang Zeng
69a6699b663a122a7ce667a5
Yujun Cai
69a6699b663a122a7ce667a6
Gang Yu
69a6699b663a122a7ce667a7
Xingjun Ma
OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens
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.
OmniLottie framework generates high-quality vector animations from multi-modal instructions using a specialized Lottie tokenizer and pretrained vision-language models.
Lottie tokenizer vision language models multi-modal interleaved instructions vector animations JSON files structured sequences animation generation