We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| dongjiahua/vica-nerf | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| radial_and_tangential_distort | Ran | dongjiahua/vica-nerf/vica/vica_utils.py pointer only (licence: NONE) · get_code("5e08bdc6036a33c2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce ViCA-NeRF, the first view-consistency-aware method for 3D editing with text instructions. In addition to the implicit neural radiance field (NeRF) modeling, our key insight is to exploit two sources of regularization that explicitly propagate the editing information across different views, thus ensuring multi-view consistency. For geometric regularization, we leverage the depth information derived from NeRF to establish image correspondences between different views. For learned regularization, we align the latent codes in the 2D diffusion model between edited and unedited images, enabling us to edit key views and propagate the update throughout the entire scene. Incorporating these two strategies, our ViCA-NeRF operates in two stages. In the initial stage, we blend edits from different views to create a preliminary 3D edit. This is followed by a second stage of NeRF training, dedicated to further refining the scene's appearance. Experimental results demonstrate that ViCA-NeRF provides more flexible, efficient (3 times faster) editing with higher levels of consistency and details, compared with the state of the art. Our code is publicly available.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.00864")
get_code_for_paper("2402.00864")
have("2402.00864")
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