We lifted 6 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 |
|---|---|---|
| shanemankiw/panodiff | canonical | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| default_loader | Ran | shanemankiw/panodiff/RelativeRotation/dataset.py pointer only (licence: NONE) · get_code("7bf07d5f59aae36d") |
| load_rgb | Ran | shanemankiw/panodiff/process_raw_BLIP.py pointer only (licence: NONE) · get_code("d6e31e4288494a9b") |
| pil_loader | Ran | shanemankiw/panodiff/RelativeRotation/dataset.py pointer only (licence: NONE) · get_code("f321f54723433661") |
| accimage_loader | Not yet run | shanemankiw/panodiff/RelativeRotation/dataset.py pointer only (licence: NONE) · get_code("404fb2b2daa1ae78") |
| get_state_dict | Not yet run | shanemankiw/panodiff/cldm/model.py pointer only (licence: NONE) · get_code("cfc17707f35e7ec4") |
| load_state_dict | Not yet run | shanemankiw/panodiff/cldm/model.py pointer only (licence: NONE) · get_code("df733a879693145d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
360$^\circ$ panoramas are extensively utilized as environmental light sources in computer graphics. However, capturing a 360$^\circ$ $\times$ 180$^\circ$ panorama poses challenges due to the necessity of specialized and costly equipment, and additional human resources. Prior studies develop various learning-based generative methods to synthesize panoramas from a single Narrow Field-of-View (NFoV) image, but they are limited in alterable input patterns, generation quality, and controllability. To address these issues, we propose a novel pipeline called PanoDiff, which efficiently generates complete 360$^\circ$ panoramas using one or more unregistered NFoV images captured from arbitrary angles. Our approach has two primary components to overcome the limitations. Firstly, a two-stage angle prediction module to handle various numbers of NFoV inputs. Secondly, a novel latent diffusion-based panorama generation model uses incomplete panorama and text prompts as control signals and utilizes several geometric augmentation schemes to ensure geometric properties in generated panoramas. Experiments show that PanoDiff achieves state-of-the-art panoramic generation quality and high controllability, making it suitable for applications such as content editing.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.14686")
get_code_for_paper("2308.14686")
have("2308.14686")
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