VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
Ajay Jain, Amber Xie, Pieter Abbeel
2023年份
62顶会引用
摘要
Figure 1. Text-to-SVG with VectorFusion. When (a) raster graphics sampled from Stable Diffusion are (b) auto-traced, they lose details that are hard to represent within the constraints of the abstraction. (c-d) VectorFusion improves fidelity and consistency with the caption by directly optimizing paths with a distillation-based diffusion loss. Find videos and more results at https://ajayj.com/vectorfusion .
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引用它的顶会 Paper62
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- OmniSVG: A Unified Scalable Vector Graphics Generation ModelYiying Yang, Wei Cheng, Sijin Chen, Xianfang Zeng 等NeurIPS 2025 · 被引用 90 次
- Word-As-Image for Semantic TypographyShir Iluz, Yael Vinker, Amir Hertz, Daniel Berio 等SIGGRAPH 2023 · 被引用 67 次
- AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZJonas Belouadi, Anne Lauscher, Steffen EgerICLR 2024 · 被引用 64 次
它引用的顶会 Paper18
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- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
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- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
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