We lifted 10 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| YadiraF/PRNet | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| P2sRt | Ran | YadiraF/PRNet/utils/estimate_pose.py code served (permissive licence) · get_code("07326d46bad4e31e") |
| get_point_weight | Ran | YadiraF/PRNet/utils/render.py code served (permissive licence) · get_code("50572306e7c01f27") |
| isPointInTri | Ran | YadiraF/PRNet/utils/render.py code served (permissive licence) · get_code("05f23476146a0cfe") |
| isRotationMatrix | Ran | YadiraF/PRNet/utils/estimate_pose.py code served (permissive licence) · get_code("5d64cc8f381e2dbd") |
| matrix2angle | Ran | YadiraF/PRNet/utils/estimate_pose.py code served (permissive licence) · get_code("b3226b96c3df432a") |
| plot_kpt | Ran | YadiraF/PRNet/utils/cv_plot.py code served (permissive licence) · get_code("eb07d94367663cae") |
| plot_vertices | Ran | YadiraF/PRNet/utils/cv_plot.py code served (permissive licence) · get_code("273ff544685cb7f1") |
| render_texture | Ran | YadiraF/PRNet/utils/render.py code served (permissive licence) · get_code("51145c8552370f7c") |
| frontalize | Not yet run | YadiraF/PRNet/utils/rotate_vertices.py code served (permissive licence) · get_code("2c47072c128b71f5") |
| plot_pose_box | Not yet run | YadiraF/PRNet/utils/cv_plot.py code served (permissive licence) · get_code("e90ac38cb687dd7d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment. To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image. We also integrate a weight mask into the loss function during training to improve the performance of the network. Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning. Meanwhile, our network is very light-weighted and spends only 9.8ms to process an image, which is extremely faster than previous works. Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.07835")
get_code_for_paper("1803.07835")
have("1803.07835")
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