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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.05722 |
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| _version_ | 1866917191827324928 |
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| author | Wang, Jin Lu, Jianxiang Chen, Comi Xu, Guangzheng Yang, Haoyu Chen, Peng Zhang, Na Xu, Yifan Wu, Longhuang Shao, Shuai Lu, Qinglin Luo, Ping |
| author_facet | Wang, Jin Lu, Jianxiang Chen, Comi Xu, Guangzheng Yang, Haoyu Chen, Peng Zhang, Na Xu, Yifan Wu, Longhuang Shao, Shuai Lu, Qinglin Luo, Ping |
| contents | Generating high-quality 3D characters from single images remains a significant challenge in digital content creation, particularly due to complex body poses and self-occlusion. In this paper, we present RCM (Rotate your Character Model), an advanced image-to-video diffusion framework tailored for high-quality novel view synthesis (NVS) and 3D character generation. Compared to existing diffusion-based approaches, RCM offers several key advantages: (1) transferring characters with any complex poses into a canonical pose, enabling consistent novel view synthesis across the entire viewing orbit, (2) high-resolution orbital video generation at 1024x1024 resolution, (3) controllable observation positions given different initial camera poses, and (4) multi-view conditioning supporting up to 4 input images, accommodating diverse user scenarios. Extensive experiments demonstrate that RCM outperforms state-of-the-art methods in both novel view synthesis and 3D generation quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05722 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation Wang, Jin Lu, Jianxiang Chen, Comi Xu, Guangzheng Yang, Haoyu Chen, Peng Zhang, Na Xu, Yifan Wu, Longhuang Shao, Shuai Lu, Qinglin Luo, Ping Computer Vision and Pattern Recognition Generating high-quality 3D characters from single images remains a significant challenge in digital content creation, particularly due to complex body poses and self-occlusion. In this paper, we present RCM (Rotate your Character Model), an advanced image-to-video diffusion framework tailored for high-quality novel view synthesis (NVS) and 3D character generation. Compared to existing diffusion-based approaches, RCM offers several key advantages: (1) transferring characters with any complex poses into a canonical pose, enabling consistent novel view synthesis across the entire viewing orbit, (2) high-resolution orbital video generation at 1024x1024 resolution, (3) controllable observation positions given different initial camera poses, and (4) multi-view conditioning supporting up to 4 input images, accommodating diverse user scenarios. Extensive experiments demonstrate that RCM outperforms state-of-the-art methods in both novel view synthesis and 3D generation quality. |
| title | Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.05722 |