awesome-style-transfer
github.com/neptune-t/awesome-style-transfer ↗A comprehensive collection of papers and datasets on generative models and their applications in style transfer across image, text, 3D, and video domains.
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🗂️ Papers by Style Carrier📚 Datasets
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me 1. 🎯 style as optimization objective resources from awesome-style-transfer"
Installation instructions →What's inside
📚 Datasets
🗂️ Papers by Style Carrier
- 3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation1. 🎯 Style as Optimization Objective
CVPR
- A closed-form solution to photorealistic image stylization2. 🧩 Style as Feature Operator
ECCV
- AdaAttN: Revisit attention mechanism in arbitrary neural style transfer2. 🧩 Style as Feature Operator
ICCV
- A Learned Representation for Artistic Style4. ⚙️ Style as Model Parameters
ICLR
- Alias-Free Generative Adversarial Networks3. 🌀 Style as Latent Variable
NeurIPS
- Analyzing and Improving the Image Quality of StyleGAN3. 🌀 Style as Latent Variable
CVPR
Showing a sample of 130 resources. View the full list on GitHub →