EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909848352849920 |
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| author | Qu, Huaizhi Zhang, Ruichen Luo, Shuqing Qi, Luchao Zhang, Zhihao Liu, Xiaoming Sengupta, Roni Chen, Tianlong |
| author_facet | Qu, Huaizhi Zhang, Ruichen Luo, Shuqing Qi, Luchao Zhang, Zhihao Liu, Xiaoming Sengupta, Roni Chen, Tianlong |
| contents | Recent advances in foundation models have driven remarkable progress in image editing, yet their extension to 3D editing remains underexplored. A natural approach is to replace the image editing modules in existing workflows with foundation models. However, their heavy computational demands and the restrictions and costs of closed-source APIs make plugging these models into existing iterative editing strategies impractical. To address this limitation, we propose EditCast3D, a pipeline that employs video generation foundation models to propagate edits from a single first frame across the entire dataset prior to reconstruction. While editing propagation enables dataset-level editing via video models, its consistency remains suboptimal for 3D reconstruction, where multi-view alignment is essential. To overcome this, EditCast3D introduces a view selection strategy that explicitly identifies consistent and reconstruction-friendly views and adopts feedforward reconstruction without requiring costly refinement. In combination, the pipeline both minimizes reliance on expensive image editing and mitigates prompt ambiguities that arise when applying foundation models independently across images. We evaluate EditCast3D on commonly used 3D editing datasets and compare it against state-of-the-art 3D editing baselines, demonstrating superior editing quality and high efficiency. These results establish EditCast3D as a scalable and general paradigm for integrating foundation models into 3D editing pipelines. The code is available at https://github.com/UNITES-Lab/EditCast3D |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection Qu, Huaizhi Zhang, Ruichen Luo, Shuqing Qi, Luchao Zhang, Zhihao Liu, Xiaoming Sengupta, Roni Chen, Tianlong Computer Vision and Pattern Recognition Recent advances in foundation models have driven remarkable progress in image editing, yet their extension to 3D editing remains underexplored. A natural approach is to replace the image editing modules in existing workflows with foundation models. However, their heavy computational demands and the restrictions and costs of closed-source APIs make plugging these models into existing iterative editing strategies impractical. To address this limitation, we propose EditCast3D, a pipeline that employs video generation foundation models to propagate edits from a single first frame across the entire dataset prior to reconstruction. While editing propagation enables dataset-level editing via video models, its consistency remains suboptimal for 3D reconstruction, where multi-view alignment is essential. To overcome this, EditCast3D introduces a view selection strategy that explicitly identifies consistent and reconstruction-friendly views and adopts feedforward reconstruction without requiring costly refinement. In combination, the pipeline both minimizes reliance on expensive image editing and mitigates prompt ambiguities that arise when applying foundation models independently across images. We evaluate EditCast3D on commonly used 3D editing datasets and compare it against state-of-the-art 3D editing baselines, demonstrating superior editing quality and high efficiency. These results establish EditCast3D as a scalable and general paradigm for integrating foundation models into 3D editing pipelines. The code is available at https://github.com/UNITES-Lab/EditCast3D |
| title | EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.13652 |