Learning Implicit Glyph Shape Representation
- URL: http://arxiv.org/abs/2106.08573v1
- Date: Wed, 16 Jun 2021 06:42:55 GMT
- Title: Learning Implicit Glyph Shape Representation
- Authors: Ying-Tian Liu, Yuan-Chen Guo, Yi-Xiao Li, Chen Wang, Song-Hai Zhang
- Abstract summary: We present a novel implicit glyph shape representation, which glyphs as shape primitives enclosed quadratic curves, and naturally enables generating glyph images at arbitrary high resolutions.
Based on the proposed representation, we design a simple yet effective disentangled network for the challenging one-shot font style transfer problem.
- Score: 6.413829791927052
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a novel implicit glyph shape representation, which
models glyphs as shape primitives enclosed by quadratic curves, and naturally
enables generating glyph images at arbitrary high resolutions. Experiments on
font reconstruction and interpolation tasks verified that this structured
implicit representation is suitable for describing both structure and style
features of glyphs. Furthermore, based on the proposed representation, we
design a simple yet effective disentangled network for the challenging one-shot
font style transfer problem, and achieve the best results comparing to
state-of-the-art alternatives in both quantitative and qualitative comparisons.
Benefit from this representation, our generated glyphs have the potential to be
converted to vector fonts through post-processing, reducing the gap between
rasterized images and vector graphics. We hope this work can provide a powerful
tool for 2D shape analysis and synthesis, and inspire further exploitation in
implicit representations for 2D shape modeling.
Related papers
- DualVector: Unsupervised Vector Font Synthesis with Dual-Part
Representation [43.64428946288288]
Current font synthesis methods fail to represent the shape concisely or require vector supervision during training.
We propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs.
Our method, named Dual-of-Font-art, outperforms state-of-the-art methods for practical use.
arXiv Detail & Related papers (2023-05-17T08:18:06Z) - VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector
Fonts via Signed Distance Functions [15.47282857047361]
This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts.
Based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves.
arXiv Detail & Related papers (2023-03-22T16:14:39Z) - ISS: Image as Stetting Stone for Text-Guided 3D Shape Generation [91.37036638939622]
This paper presents a new framework called Image as Stepping Stone (ISS) for the task by introducing 2D image as a stepping stone to connect the two modalities.
Our key contribution is a two-stage feature-space-alignment approach that maps CLIP features to shapes.
We formulate a text-guided shape stylization module to dress up the output shapes with novel textures.
arXiv Detail & Related papers (2022-09-09T06:54:21Z) - 3DILG: Irregular Latent Grids for 3D Generative Modeling [44.16807313707137]
We propose a new representation for encoding 3D shapes as neural fields.
The representation is designed to be compatible with the transformer architecture and to benefit both shape reconstruction and shape generation.
arXiv Detail & Related papers (2022-05-27T11:29:52Z) - DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality
Learning [21.123297001902177]
We propose a novel method, DeepVecFont, to generate visually-pleasing vector glyphs.
The highlights of this paper are threefold. First, we design a dual-modality learning strategy which utilizes both image-aspect and sequence-aspect features of fonts to synthesize vector glyphs.
Second, we provide a new generative paradigm to handle unstructured data (e.g., vector glyphs) by randomly sampling plausible results to get the optimal one which is further refined under the guidance of generated structured data.
arXiv Detail & Related papers (2021-10-13T12:57:19Z) - Scalable Font Reconstruction with Dual Latent Manifolds [55.29525824849242]
We propose a deep generative model that performs typography analysis and font reconstruction.
Our approach enables us to massively scale up the number of character types we can effectively model.
We evaluate on the task of font reconstruction over various datasets representing character types of many languages.
arXiv Detail & Related papers (2021-09-10T20:37:43Z) - Font Completion and Manipulation by Cycling Between Multi-Modality
Representations [113.26243126754704]
We innovate to explore the generation of font glyphs as 2D graphic objects with the graph as an intermediate representation.
We formulate a cross-modality cycled image-to-image structure with a graph between an image encoder and an image.
Our model generates improved results than both image-to-image baseline and previous state-of-the-art methods for glyph completion.
arXiv Detail & Related papers (2021-08-30T02:43:29Z) - A Multi-Implicit Neural Representation for Fonts [79.6123184198301]
font-specific discontinuities like edges and corners are difficult to represent using neural networks.
We introduce textitmulti-implicits to represent fonts as a permutation-in set of learned implict functions, without losing features.
arXiv Detail & Related papers (2021-06-12T21:40:11Z) - Learning to Caricature via Semantic Shape Transform [95.25116681761142]
We propose an algorithm based on a semantic shape transform to produce shape exaggerations.
We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures.
arXiv Detail & Related papers (2020-08-12T03:41:49Z) - DualSDF: Semantic Shape Manipulation using a Two-Level Representation [54.62411904952258]
We propose DualSDF, a representation expressing shapes at two levels of granularity, one capturing fine details and the other representing an abstracted proxy shape.
Our two-level model gives rise to a new shape manipulation technique in which a user can interactively manipulate the coarse proxy shape and see the changes instantly mirrored in the high-resolution shape.
arXiv Detail & Related papers (2020-04-06T17:59:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.