Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Neural Doodle | 9,399 | 4 years ago | 50 | agpl-3.0 | Python | |||||
Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.) | ||||||||||
Texture Vs Shape | 728 | a year ago | other | R | ||||||
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral) | ||||||||||
Stylized Imagenet | 485 | 4 months ago | 1 | mit | Python | |||||
Code to create Stylized-ImageNet, a stylized version of standard ImageNet (ICLR 2019 Oral) | ||||||||||
Torch Encoding Layer | 82 | 3 years ago | 2 | Lua | ||||||
Deep Texture Encoding Network | ||||||||||
3d_appearance_sr | 77 | 3 years ago | 6 | mit | Python | |||||
This is the official website of our work 3D Appearance Super-Resolution with Deep Learning published on CVPR2019. | ||||||||||
Deep Mvlm | 52 | 2 years ago | 4 | mit | Python | |||||
A tool for precisely placing 3D landmarks on 3D facial scans based on the paper "Multi-view Consensus CNN for 3D Facial Landmark Placement" | ||||||||||
Neuraltexture | 45 | 4 years ago | 1 | mit | Python | |||||
Learning a Neural 3D Texture Space from 2D Exemplars [CVPR 2020] | ||||||||||
Deep Encoding Pooling Network Dep | 29 | 6 years ago | 2 | Python | ||||||
Code release for "Deep Texture Manifold for Ground Terrain Recognition", CVPR 2018 | ||||||||||
Surfacenet | 28 | 6 months ago | 2 | mit | Python | |||||
The official PyTorch implementation for paper "SurfaceNet: Adversarial SVBRDF Estimation from a Single Image" | ||||||||||
Normal Map Generator | 26 | 7 months ago | mit | Python | ||||||
Generate a normal map or displacement map from a photo texture with UNet |