Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912535108648960 |
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| author | Xu, Hongbin Yu, Chaohui Xiao, Feng Xing, Jiazheng Ci, Hai Chen, Weitao Wang, Fan Li, Ming |
| author_facet | Xu, Hongbin Yu, Chaohui Xiao, Feng Xing, Jiazheng Ci, Hai Chen, Weitao Wang, Fan Li, Ming |
| contents | Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose \name{}, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. \emph{View consistency} ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. \emph{Condition consistency} aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that \name{} significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17\% PSNR for edge, +6.26\% PSNR for sketch). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14975 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization Xu, Hongbin Yu, Chaohui Xiao, Feng Xing, Jiazheng Ci, Hai Chen, Weitao Wang, Fan Li, Ming Computer Vision and Pattern Recognition Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose \name{}, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. \emph{View consistency} ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. \emph{Condition consistency} aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that \name{} significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17\% PSNR for edge, +6.26\% PSNR for sketch). |
| title | Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.14975 |