We lifted 11 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches | pwc_unofficial | 9 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| binary | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/depthestimate/BatchFetcher.py code served (permissive licence) · get_code("2c9fb04fc48a5a74") |
| get_norm_layer | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/models/networks.py code served (permissive licence) · get_code("b27dffc0609fbec0") |
| get_scheduler | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/models/networks.py code served (permissive licence) · get_code("f28f371ac79d4e8a") |
| heatmap | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/depthestimate/visualizeptexample.v.py code served (permissive licence) · get_code("f4f8efe70b76b9b4") |
| init_net | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/models/networks.py code served (permissive licence) · get_code("bf329cef80ee24c5") |
| mls_affine_deformation | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/img_utils.py code served (permissive licence) · get_code("28f5dea7ad7deca1") |
| mls_affine_deformation_1pt | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/img_utils.py code served (permissive licence) · get_code("362739607d36b783") |
| rgb2gray | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/depthestimate/BatchFetcher.py code served (permissive licence) · get_code("eb7801586aa2560a") |
| rgba2rgb | Ran | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/depthestimate/BatchFetcher.py code served (permissive licence) · get_code("eaa69b40ef46f8c4") |
| mls_affine_deformation_inv | Not yet run | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/sketchstd/img_utils.py code served (permissive licence) · get_code("96ae243e2e93aeb5") |
| showpoints | Not yet run | samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches/depthestimate/show3d.py code served (permissive licence) · get_code("cc0e2a063a44e5cf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Humans can envision a realistic photo given a free-hand sketch that is not only spatially imprecise and geometrically distorted but also without colors and visual details. We study unsupervised sketch-to-photo synthesis for the first time, learning from unpaired sketch-photo data where the target photo for a sketch is unknown during training. Existing works only deal with style change or spatial deformation alone, synthesizing photos from edge-aligned line drawings or transforming shapes within the same modality, e.g., color images. Our key insight is to decompose unsupervised sketch-to-photo synthesis into a two-stage translation task: First shape translation from sketches to grayscale photos and then content enrichment from grayscale to color photos. We also incorporate a self-supervised denoising objective and an attention module to handle abstraction and style variations that are inherent and specific to sketches. Our synthesis is sketch-faithful and photo-realistic to enable sketch-based image retrieval in practice. An exciting corollary product is a universal and promising sketch generator that captures human visual perception beyond the edge map of a photo.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1909.08313")
get_code_for_paper("1909.08313")
have("1909.08313")
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