We lifted 18 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| junwuzhang19/repaint123 | canonical | 11 of 18 |
| Function | Status | Where it lives |
|---|---|---|
| build_transform | Ran | junwuzhang19/repaint123/main2.py code served (permissive licence) · get_code("57e95166ade5f25c") |
| dynamic_preprocess | Ran | junwuzhang19/repaint123/main2.py code served (permissive licence) · get_code("d610b5eabe0c9db1") |
| find_closest_aspect_ratio | Ran | junwuzhang19/repaint123/main2.py code served (permissive licence) · get_code("bd77f5f8067f18e9") |
| get_expon_lr_func | Ran | junwuzhang19/repaint123/gs_renderer.py code served (permissive licence) · get_code("6d2c15014d993b0e") |
| inverse_sigmoid | Ran | junwuzhang19/repaint123/gs_renderer.py code served (permissive licence) · get_code("b488da571728b636") |
| numpy_to_torch | Ran | junwuzhang19/repaint123/metric_utils.py code served (permissive licence) · get_code("c999c85b86b93312") |
| optimize_stage_1 | Ran | junwuzhang19/repaint123/gradio_app.py code served (permissive licence) · get_code("45a2b5251ba4d0ef") |
| optimize_stage_2 | Ran | junwuzhang19/repaint123/gradio_app.py code served (permissive licence) · get_code("ea1786674cbb3dca") |
| safe_normalize | Ran | junwuzhang19/repaint123/mesh.py code served (permissive licence) · get_code("fe9e0158ece7e066") |
| score_from_method_for_dataset | Ran | junwuzhang19/repaint123/metric_utils.py code served (permissive licence) · get_code("2dfc991848acce36") |
| strip_lowerdiag | Ran | junwuzhang19/repaint123/gs_renderer.py code served (permissive licence) · get_code("cd789cab1b072db4") |
| dot | Not yet run | junwuzhang19/repaint123/cam_utils.py code served (permissive licence) · get_code("e35aafbf0069edb6") |
| length | Not yet run | junwuzhang19/repaint123/cam_utils.py code served (permissive licence) · get_code("43cdc168f88caf9b") |
| poisson_mesh_reconstruction | Not yet run | junwuzhang19/repaint123/mesh_utils.py code served (permissive licence) · get_code("b7f0e912438abda8") |
| safe_normalize | Not yet run | junwuzhang19/repaint123/cam_utils.py code served (permissive licence) · get_code("8a8484ccf552d87f") |
| scale_img_hwc | Not yet run | junwuzhang19/repaint123/mesh_renderer.py code served (permissive licence) · get_code("1eb01adb2d4db29c") |
| scale_img_nhw | Not yet run | junwuzhang19/repaint123/mesh_renderer.py code served (permissive licence) · get_code("7d11ee5002c743ed") |
| scale_img_nhwc | Not yet run | junwuzhang19/repaint123/mesh_renderer.py code served (permissive licence) · get_code("e3e7b5863ff869ea") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent one image to 3D generation methods commonly adopt Score Distillation Sampling (SDS). Despite the impressive results, there are multiple deficiencies including multi-view inconsistency, over-saturated and over-smoothed textures, as well as the slow generation speed. To address these deficiencies, we present Repaint123 to alleviate multi-view bias as well as texture degradation and speed up the generation process. The core idea is to combine the powerful image generation capability of the 2D diffusion model and the texture alignment ability of the repainting strategy for generating high-quality multi-view images with consistency. We further propose visibility-aware adaptive repainting strength for overlap regions to enhance the generated image quality in the repainting process. The generated high-quality and multi-view consistent images enable the use of simple Mean Square Error (MSE) loss for fast 3D content generation. We conduct extensive experiments and show that our method has a superior ability to generate high-quality 3D content with multi-view consistency and fine textures in 2 minutes from scratch. Our project page is available at https://pku-yuangroup.github.io/repaint123/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.13271")
get_code_for_paper("2312.13271")
have("2312.13271")
Connect an agent — have() is free.