StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
To leverage LLMs for visual synthesis, traditional methods convert raster
image information into discrete grid tokens through specialized visual modules,
while disrupting the model's ability to capture the true semantic
representation of visual scenes. This paper posits that an alternative
representation of images, vector graphics, can effectively surmount this
limitation by enabling a more natural and semantically coherent segmentation of
the image information. Thus, we introduce StrokeNUWA, a pioneering work
exploring a better visual representation ''stroke tokens'' on vector graphics,
which is inherently visual semantics rich, naturally compatible with LLMs, and
highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly
surpass traditional LLM-based and optimization-based methods across various
metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up
to a 94x speedup in inference over the speed of prior methods with an
exceptional SVG code compression ratio of 6.9%.
65b9d04ba1880eed44a82604
Zecheng Tang
admin_assigned
2024-01-31T09:43:30.681Z
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Zecheng Tang
user
ZetangForward
65b9d04ba1880eed44a82605
Chenfei Wu
admin_assigned
2024-01-31T09:43:45.231Z
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Chenfei Wu
user
Chenfei
65b9d04ba1880eed44a82606
Zekai Zhang
65b9d04ba1880eed44a82607
Mingheng Ni
65b9d04ba1880eed44a82608
Shengming Yin
65b9d04ba1880eed44a82609
Yu Liu
65b9d04ba1880eed44a8260a
Zhengyuan Yang
admin_assigned
2024-01-31T09:45:46.708Z
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Zhengyuan Yang
user
zyang39
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Lijuan Wang
admin_assigned
2024-01-31T09:46:42.774Z
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Lijuan Wang
user
Lijuan
65b9d04ba1880eed44a8260c
Zicheng Liu
admin_assigned
2024-01-31T09:46:52.108Z
6569f6cb44ce94a70187f407
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Zicheng Liu
user
ZCLiu35
65b9d04ba1880eed44a8260d
Juntao Li
admin_assigned
2024-01-31T09:47:09.323Z
60d1d7f94672e8c13e7c2bce
/avatars/be3a3990492ef1c5f3ff33cc043ae166.svg
Juntao Li
user
ljtsuda
65b9d04ba1880eed44a8260e
Nan Duan
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
To leverage LLMs for visual synthesis, traditional methods convert raster
image information into discrete grid tokens through specialized visual modules,
while disrupting the model's ability to capture the true semantic
representation of visual scenes. This paper posits that an alternative
representation of images, vector graphics, can effectively surmount this
limitation by enabling a more natural and semantically coherent segmentation of
the image information. Thus, we introduce StrokeNUWA, a pioneering work
exploring a better visual representation ''stroke tokens'' on vector graphics,
which is inherently visual semantics rich, naturally compatible with LLMs, and
highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly
surpass traditional LLM-based and optimization-based methods across various
metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up
to a 94x speedup in inference over the speed of prior methods with an
exceptional SVG code compression ratio of 6.9%.
StrokeNUWA introduces stroke tokens for vector graphic representation, enhancing semantic coherence in LLM-based visual synthesis and achieving significant speed and compression improvements.
LLMs vector graphics stroke tokens semantic representation visual semantics rich SVG code compression