We lifted 18 functions out of this paper's own repositories and ran 17 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 |
|---|---|---|
| LMozart/ECCV2024-GCS-BEG | canonical | 17 of 18 |
| Function | Status | Where it lives |
|---|---|---|
| interpolate_fn | Ran | LMozart/ECCV2024-GCS-BEG/guidance/solver.py code served (permissive licence) · get_code("6a35b62fbc80f70a") |
| PILtoTorch | Ran | LMozart/ECCV2024-GCS-BEG/utils/general_utils.py code served (permissive licence) · get_code("95abae1a2b93399d") |
| batch_get_perpendicular_component | Ran | LMozart/ECCV2024-GCS-BEG/guidance/perpneg_utils.py code served (permissive licence) · get_code("fb9454cc0151a3f9") |
| ddim_add_noise | Ran | LMozart/ECCV2024-GCS-BEG/guidance/sd_step.py code served (permissive licence) · get_code("182ef37772d9a6e2") |
| extract_into_tensor | Ran | LMozart/ECCV2024-GCS-BEG/guidance/solver.py code served (permissive licence) · get_code("a15cfd7932844ef9") |
| gaussian | Ran | LMozart/ECCV2024-GCS-BEG/utils/loss_utils.py code served (permissive licence) · get_code("c56b7ef16f309a45") |
| get_perpendicular_component | Ran | LMozart/ECCV2024-GCS-BEG/guidance/perpneg_utils.py code served (permissive licence) · get_code("8b06a657ede08dde") |
| get_rays_torch | Ran | LMozart/ECCV2024-GCS-BEG/scene/cameras.py code served (permissive licence) · get_code("53a289e51949870a") |
| inverse_sigmoid | Ran | LMozart/ECCV2024-GCS-BEG/utils/general_utils.py code served (permissive licence) · get_code("b488da571728b636") |
| inverse_sigmoid_np | Ran | LMozart/ECCV2024-GCS-BEG/utils/general_utils.py code served (permissive licence) · get_code("700436eebf1e003c") |
| l1_loss | Ran | LMozart/ECCV2024-GCS-BEG/utils/loss_utils.py code served (permissive licence) · get_code("ac0e42d6fbcfbbe6") |
| l2_loss | Ran | LMozart/ECCV2024-GCS-BEG/utils/loss_utils.py code served (permissive licence) · get_code("8c3b0f873ba11813") |
| predicted_origin | Ran | LMozart/ECCV2024-GCS-BEG/guidance/solver.py code served (permissive licence) · get_code("6df07632ebc649de") |
| randn_tensor | Ran | LMozart/ECCV2024-GCS-BEG/guidance/sd_step.py code served (permissive licence) · get_code("1f22666d0ffc07f3") |
| rgb2sat | Ran | LMozart/ECCV2024-GCS-BEG/guidance/gcs_utils.py code served (permissive licence) · get_code("dd19422c076362d7") |
| safe_normalize | Ran | LMozart/ECCV2024-GCS-BEG/scene/dataset_readers.py code served (permissive licence) · get_code("1c9cf033d080b2e0") |
| weighted_perpendicular_aggregator | Ran | LMozart/ECCV2024-GCS-BEG/guidance/perpneg_utils.py code served (permissive licence) · get_code("b216ea040465b27a") |
| ddim_step | Not yet run | LMozart/ECCV2024-GCS-BEG/guidance/sd_step.py code served (permissive licence) · get_code("72dee17b2e482ad4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Although recent advancements in text-to-3D generation have significantly improved generation quality, issues like limited level of detail and low fidelity still persist, which requires further improvement. To understand the essence of those issues, we thoroughly analyze current score distillation methods by connecting theories of consistency distillation to score distillation. Based on the insights acquired through analysis, we propose an optimization framework, Guided Consistency Sampling (GCS), integrated with 3D Gaussian Splatting (3DGS) to alleviate those issues. Additionally, we have observed the persistent oversaturation in the rendered views of generated 3D assets. From experiments, we find that it is caused by unwanted accumulated brightness in 3DGS during optimization. To mitigate this issue, we introduce a Brightness-Equalized Generation (BEG) scheme in 3DGS rendering. Experimental results demonstrate that our approach generates 3D assets with more details and higher fidelity than state-of-the-art methods. The codes are released at https://github.com/LMozart/ECCV2024-GCS-BEG.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.13584")
get_code_for_paper("2407.13584")
have("2407.13584")
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