HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers

• URL: http://arxiv.org/abs/2506.03118v1
• Date: Tue, 03 Jun 2025 17:50:05 GMT
• Title: HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers
• Authors: Zhiyuan Yu, Zhe Li, Hujun Bao, Can Yang, Xiaowei Zhou,
• Abstract summary: HumanRAM is a novel feed-forward approach for generalizable human reconstruction and animation from monocular or sparse human images.<n>Our approach integrates human reconstruction and animation into a unified framework by introducing explicit pose conditions.<n> Experiments show that HumanRAM significantly surpasses previous methods in terms of reconstruction accuracy, animation fidelity, and generalization performance on real-world datasets.
• Score: 60.86393841247567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human reconstruction and animation are long-standing topics in computer graphics and vision. However, existing methods typically rely on sophisticated dense-view capture and/or time-consuming per-subject optimization procedures. To address these limitations, we propose HumanRAM, a novel feed-forward approach for generalizable human reconstruction and animation from monocular or sparse human images. Our approach integrates human reconstruction and animation into a unified framework by introducing explicit pose conditions, parameterized by a shared SMPL-X neural texture, into transformer-based large reconstruction models (LRM). Given monocular or sparse input images with associated camera parameters and SMPL-X poses, our model employs scalable transformers and a DPT-based decoder to synthesize realistic human renderings under novel viewpoints and novel poses. By leveraging the explicit pose conditions, our model simultaneously enables high-quality human reconstruction and high-fidelity pose-controlled animation. Experiments show that HumanRAM significantly surpasses previous methods in terms of reconstruction accuracy, animation fidelity, and generalization performance on real-world datasets. Video results are available at https://zju3dv.github.io/humanram/.

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