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Found 2 papers, 1 papers with code
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
This unsupervised approach allows the generated data to capture the vast complexity of the real world while maintaining the precision and scale of synthetic data.
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists.
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