DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915669837086720 |
|---|---|
| author | Zhang, Peiying Zhao, Nanxuan Fisher, Matthew Xu, Yiran Liao, Jing Liu, Difan |
| author_facet | Zhang, Peiying Zhao, Nanxuan Fisher, Matthew Xu, Yiran Liao, Jing Liu, Difan |
| contents | Recent vision-language model (VLM)-based approaches have achieved impressive results on SVG generation. However, because they generate only text and lack visual signals during decoding, they often struggle with complex semantics and fail to produce visually appealing or geometrically coherent SVGs. We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner. DuetSVG is trained on both image and SVG datasets. At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality. Extensive experiments show that our method outperforms existing methods, producing visually faithful, semantically aligned, and syntactically clean SVGs across a wide range of applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance Zhang, Peiying Zhao, Nanxuan Fisher, Matthew Xu, Yiran Liao, Jing Liu, Difan Computer Vision and Pattern Recognition Recent vision-language model (VLM)-based approaches have achieved impressive results on SVG generation. However, because they generate only text and lack visual signals during decoding, they often struggle with complex semantics and fail to produce visually appealing or geometrically coherent SVGs. We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner. DuetSVG is trained on both image and SVG datasets. At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality. Extensive experiments show that our method outperforms existing methods, producing visually faithful, semantically aligned, and syntactically clean SVGs across a wide range of applications. |
| title | DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.10894 |