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2020 CVPR CVPR 2020

Learning a Neural 3D Texture Space From 2D Exemplars

Abstract

We suggest a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to learned, deep, non-linearities. Our model encodes all exemplars from a diverse set of textures without a need to be re-trained for each exemplar. Applications include texture interpolation, and learning 3D textures from 2D exemplars.

🌉 Interdisciplinary Bridge — Computer Science and Computer Vision and Deep Learning and Machine Learning
🧭 Keyword Pioneer — neural texture
🐣 Hot Topic Early Bird — neural rendering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Philipp Henzler , Niloy J. Mitra , Tobias Ritschel

Topics

Machine Learning > Core Methods > Representation Learning Deep Learning > Models > Generative Models Computer Vision > Generation > Image Generation Computer Science > Applications > Computer Graphics Computer Vision > Generation > 3D Generation

Keywords

neural rendering deep learning texture synthesis generative model 3d generation neural texture 3d texture procedural texturing neural texture synthesis
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