We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| seasonSH/LiftedGAN | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| calc_fid | Ran | seasonSH/LiftedGAN/tools/test_fid.py pointer only (licence: NONE) · get_code("e6c1de4697e7ed81") |
| extract_feature_from_generator | Not yet run | seasonSH/LiftedGAN/tools/test_fid.py pointer only (licence: NONE) · get_code("09c964b18d9120be") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in the sense that it is able to (1) disentangle the latent space of StyleGAN2 into texture, shape, viewpoint, lighting and (2) generate 3D components for rendering synthetic images. Unlike most previous methods, our method is completely self-supervised, i.e. it neither requires any manual annotation nor 3DMM model for training. Instead, it learns to generate images as well as their 3D components by distilling the prior knowledge in StyleGAN2 with a differentiable renderer. The proposed model is able to output both the 3D shape and texture, allowing explicit pose and lighting control over generated images. Qualitative and quantitative results show the superiority of our approach over existing methods on 3D-controllable GANs in content controllability while generating realistic high quality images.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2011.13126")
get_code_for_paper("2011.13126")
have("2011.13126")
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