HRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone Scans
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912675074670592 |
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| author | Shi, Chao Jia, Shenghao Liu, Jinhui Zhang, Yong Zhu, Liangchao Yang, Zhonglei Ma, Jinze Niu, Chaoyue Lv, Chengfei |
| author_facet | Shi, Chao Jia, Shenghao Liu, Jinhui Zhang, Yong Zhu, Liangchao Yang, Zhonglei Ma, Jinze Niu, Chaoyue Lv, Chengfei |
| contents | We present HRM$^2$Avatar, a framework for creating high-fidelity avatars from monocular phone scans, which can be rendered and animated in real time on mobile devices. Monocular capture with smartphones provides a low-cost alternative to studio-grade multi-camera rigs, making avatar digitization accessible to non-expert users. Reconstructing high-fidelity avatars from single-view video sequences poses challenges due to limited visual and geometric data. To address these limitations, at the data level, our method leverages two types of data captured with smartphones: static pose sequences for texture reconstruction and dynamic motion sequences for learning pose-dependent deformations and lighting changes. At the representation level, we employ a lightweight yet expressive representation to reconstruct high-fidelity digital humans from sparse monocular data. We extract garment meshes from monocular data to model clothing deformations effectively, and attach illumination-aware Gaussians to the mesh surface, enabling high-fidelity rendering and capturing pose-dependent lighting. This representation efficiently learns high-resolution and dynamic information from monocular data, enabling the creation of detailed avatars. At the rendering level, real-time performance is critical for animating high-fidelity avatars in AR/VR, social gaming, and on-device creation. Our GPU-driven rendering pipeline delivers 120 FPS on mobile devices and 90 FPS on standalone VR devices at 2K resolution, over $2.7\times$ faster than representative mobile-engine baselines. Experiments show that HRM$^2$Avatar delivers superior visual realism and real-time interactivity, outperforming state-of-the-art monocular methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_13587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone Scans Shi, Chao Jia, Shenghao Liu, Jinhui Zhang, Yong Zhu, Liangchao Yang, Zhonglei Ma, Jinze Niu, Chaoyue Lv, Chengfei Graphics We present HRM$^2$Avatar, a framework for creating high-fidelity avatars from monocular phone scans, which can be rendered and animated in real time on mobile devices. Monocular capture with smartphones provides a low-cost alternative to studio-grade multi-camera rigs, making avatar digitization accessible to non-expert users. Reconstructing high-fidelity avatars from single-view video sequences poses challenges due to limited visual and geometric data. To address these limitations, at the data level, our method leverages two types of data captured with smartphones: static pose sequences for texture reconstruction and dynamic motion sequences for learning pose-dependent deformations and lighting changes. At the representation level, we employ a lightweight yet expressive representation to reconstruct high-fidelity digital humans from sparse monocular data. We extract garment meshes from monocular data to model clothing deformations effectively, and attach illumination-aware Gaussians to the mesh surface, enabling high-fidelity rendering and capturing pose-dependent lighting. This representation efficiently learns high-resolution and dynamic information from monocular data, enabling the creation of detailed avatars. At the rendering level, real-time performance is critical for animating high-fidelity avatars in AR/VR, social gaming, and on-device creation. Our GPU-driven rendering pipeline delivers 120 FPS on mobile devices and 90 FPS on standalone VR devices at 2K resolution, over $2.7\times$ faster than representative mobile-engine baselines. Experiments show that HRM$^2$Avatar delivers superior visual realism and real-time interactivity, outperforming state-of-the-art monocular methods. |
| title | HRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone Scans |
| topic | Graphics |
| url | https://arxiv.org/abs/2510.13587 |