Awesome-Sketch-Synthesis
:books: A collection of papers about Sketch Synthesis (Generation).
https://github.com/MarkMoHR/Awesome-Sketch-Synthesis
Last synced: 6 days ago
JSON representation
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2. Sketch-Synthesis Approaches
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3) Text/Attribute-to-sketch
- VectorPainter: A Novel Approach to Stylized Vector Graphics Synthesis with Vectorized Strokes
- Text2Sketch: Learning Face Sketch from Facial Attribute Text
- Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications
- Scones: Towards Conversational Authoring of Sketches
- Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts - tokyo-workshop) [[project]](https://es-clip.github.io/) |
- CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders
- StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation
- VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
- SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation
- DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models - project/) [[code]](https://github.com/ximinng/DiffSketcher) |
- IconShop: Text-Based Vector Icon Synthesis with Autoregressive Transformers - shop.github.io/) |
- Text-Guided Vector Graphics Customization
- SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation
- Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models
- SketchAgent: Language-Driven Sequential Sketch Generation - vinker/SketchAgent) [[webpage]](https://yael-vinker.github.io/sketch-agent/) |
- SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive Canvas Layout
- SVGDreamer: Text Guided SVG Generation with Diffusion Model - project/) [[code]](https://github.com/ximinng/SVGDreamer) |
- Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications
- Text-based Vector Sketch Editing with Image Editing Diffusion Prior
- Text-to-Vector Generation with Neural Path Representation - NPR/) |
- NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation
- StarVector: Generating Scalable Vector Graphics Code from Images and Text - vector) |
- SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers
- SVGFusion: Scalable Text-to-SVG Generation via Vector Space Diffusion
- Empowering LLMs to Understand and Generate Complex Vector Graphics
- LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer
- OmniSVG: A Unified Scalable Vector Graphics Generation Model
- NeuralSVG: An Implicit Representation for Text-to-Vector Generation
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4) 3D shape-to-sketch
- DeepShapeSketch : Generating hand drawing sketches from 3D objects
- Neural Contours: Learning to Draw Lines from 3D Shapes
- Cloud2Curve: Generation and Vectorization of Parametric Sketches - 9.html) |
- Neural Strokes: Stylized Line Drawing of 3D Shapes
- Learning a Style Space for Interactive Line Drawing Synthesis from Animated 3D Models
- CAD2Sketch: Generating Concept Sketches from CAD Sequences
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5) Art-to-sketch
- Closure-aware Sketch Simplification
- StrokeAggregator: Consolidating Raw Sketches into Artist-Intended Curve Drawings
- StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters
- StripMaker: Perception-driven Learned Vector Sketch Consolidation
- Topology-Driven Vectorization of Clean Line Drawings
- A Delaunay triangulation based approach for cleaning rough sketches
- Semantic Segmentation for Line Drawing Vectorization Using Neural Networks
- Deep Line Drawing Vectorization via Line Subdivision and Topology Reconstruction
- Inertia-based Fast Vectorization of Line Drawings
- Vectorization of Line Drawings via Polyvector Fields
- General Virtual Sketching Framework for Vector Line Art
- End-to-end Line Drawing Vectorization
- Vectorizing Line Drawings of Arbitrary Thickness via Boundary-based Topology Reconstruction
- Singularity-Free Frame Fields for Line Drawing Vectorization
- A Benchmark for Rough Sketch Cleanup - Benchmark-for-Rough-Sketch-Cleanup) |
- Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup
- Mastering Sketching: Adversarial Augmentation for Structured Prediction
- Real-Time Data-Driven Interactive Rough Sketch Inking
- Manga Filling Style Conversion with Screentone Variational Autoencoder
- Generating Manga from Illustrations via Mimicking Manga Workflow
- MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing
- MARVEL: Raster Gray-level Manga Vectorization via Primitive-wise Deep Reinforcement Learning
- Inertia-based Fast Vectorization of Line Drawings
- Perceptual-aware Sketch Simplification Based on Integrated VGG Layers
- Deep Line Drawing Vectorization via Line Subdivision and Topology Reconstruction
- Deep extraction of manga structural lines
- StripMaker: Perception-driven Learned Vector Sketch Consolidation
- Region-Aware Simplification and Stylization of 3D Line Drawings
- Manga Filling Style Conversion with Screentone Variational Autoencoder
- Closure-aware Sketch Simplification
- Deep Sketch Vectorization via Implicit Surface Extraction - Sketch-Vectorization) | Hybrid |
- Fidelity vs. Simplicity: a Global Approach to Line Drawing Vectorization - sop.inria.fr/reves/Basilic/2016/FLB16/) | No |
- Integer-Grid Sketch Simplification and Vectorization - sam.inria.fr/d3/grid-vectorization/) [[Code]](https://gitlab.inria.fr/D3/grid-vectorization/) | No |
- Deep Vectorization of Technical Drawings - Vectorization-of-Technical-Drawings) | Yes |
- Keypoint-Driven Line Drawing Vectorization via PolyVector Flow - driven-polyvector-flow/) | Hybrid |
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0. Survey
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3. Vector Graphics Generation (2D)
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5) Art-to-sketch
- Segmentation-Based Parametric Painting
- Strokenet: A neural painting environment
- Neural Painters: A learned differentiable constraint for generating brushstroke paintings - painters-pytorch) |
- Learning to Sketch with Deep Q Networks and Demonstrated Strokes
- Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
- Perception-Driven Semi-Structured Boundary Vectorization
- PolyFit: Perception-aligned Vectorization of Raster Clip-art via Intermediate Polygonal Fitting
- ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis
- Image vectorization and editing via linear gradient layer decomposition
- Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization
- SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis
- Vector Graphics Generation via Mutually Impulsed Dual-domain Diffusion
- Layered Image Vectorization via Semantic Simplification
- Optimize and Reduce: A Top-Down Approach for Image Vectorization - and-reduce) |
- Synthesizing Programs for Images using Reinforced Adversarial Learning
- Unsupervised Doodling and Painting with Improved SPIRAL - to-paint.github.io/) |
- Editable Image Geometric Abstraction via Neural Primitive Assembly
- Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
- Intelli-Paint: Towards Developing More Human-Intelligible Painting Agents - paint) |
- Towards Layer-wise Image Vectorization - xu/LIVE) [[project]](https://ma-xu.github.io/LIVE/) |
- Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
- Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings - guidance) |
- Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes - parameterized-style-transfer) |
- Im2Vec: Synthesizing Vector Graphics without Vector Supervision
- Stylized Neural Painting - neural-painting) [[project]](https://jiupinjia.github.io/neuralpainter/) |
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3. Vector Graphics Generation
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5) Art-to-sketch
- Learning to Paint With Model-based Deep Reinforcement Learning - research/ICCV2019-LearningToPaint) |
- Depixelizing pixel art
- TCB-Spline-Based Image Vectorization
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4. Vector Graphics Generation (3D)