Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation

Jin Wang; Jianxiang Lu; Comi Chen; Guangzheng Xu; Haoyu Yang; Peng Chen; Na Zhang; Yifan Xu; Longhuang Wu; Shuai Shao; Qinglin Lu; Ping Luo

arXiv · 2601.05722

Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation

Jin Wang·Jianxiang Lu·Comi Chen·Guangzheng Xu·Haoyu Yang·Peng Chen·Na Zhang·Yifan Xu·Longhuang Wu·Shuai Shao·Qinglin Lu·Ping Luo

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Abstract

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.

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Jin Wang, Jianxiang Lu, Comi Chen, Guangzheng Xu, Haoyu Yang, Peng Chen, Na Zhang, Yifan Xu, Longhuang Wu, Shuai Shao, Qinglin Lu, Ping Luo. 2026-01-09. Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation. https://arxiv.org/abs/2601.05722

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