We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| liren2515/garmentrecovery | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| parse_sam | Ran | liren2515/garmentrecovery/data_prepare/step1_image_prepare.py pointer only (licence: NONE) · get_code("4c3c5388ec28e02c") |
| parse_segmentation | Ran | liren2515/garmentrecovery/data_prepare/step1_image_prepare.py pointer only (licence: NONE) · get_code("3571de0086c93a30") |
| show_anns | Ran | liren2515/garmentrecovery/data_prepare/step1_image_prepare.py pointer only (licence: NONE) · get_code("de5c12fcffe8761d") |
| get_scale_trans | Not yet run | liren2515/garmentrecovery/data_prepare/step3_bni_prepare.py pointer only (licence: NONE) · get_code("0870d48dd30c1711") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or deformation. To this end, we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations, including large ones. Not only does our approach recover the garment geometry accurately, it also yields models that can be directly used by downstream applications such as animation and simulation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.10356")
get_code_for_paper("2311.10356")
have("2311.10356")
Connect an agent — have() is free.