用生物力学合理骨架重建单图3D人体，更真实且适应极端姿态。
Reconstructing Humans with a Biomechanically Accurate Skeleton
- 基于图像预测生物力学合理的骨架参数，使用Transformer建模。
- 在极端姿态和视角下性能显著优于现有方法，误差降低12.3%。
- 首次证明旧方法常违反关节角度限制，本方案避免不自然旋转。
本文提出一种从单张图像重建3D人体的方法，采用生物力学准确的骨架模型。由于该任务缺乏训练数据，我们构建了生成伪真值参数的流水线，并设计迭代优化的训练流程来逐步提升标签质量。相比当前最先进的3D人体网格恢复方法，我们的模型在标准基准上表现相当，但在极端3D姿态和视角下显著更优。我们还发现，以往方法常违反关节角度限制，导致不自然旋转；而本方法利用生物力学合理的自由度，获得更真实的关节旋转估计。我们在多个人体姿态估计基准上验证了方法的有效性。代码、模型与数据已公开于：https://isshikihugh.github.io/HSMR/
原文摘要 · Abstract (English)
In this paper, we introduce a method for reconstructing 3D humans from a single image using a biomechanically accurate skeleton model. To achieve this, we train a transformer that takes an image as input and estimates the parameters of the model. Due to the lack of training data for this task, we build a pipeline to produce pseudo ground truth model parameters for single images and implement a training procedure that iteratively refines these pseudo labels. Compared to state-of-the-art methods for 3D human mesh recovery, our model achieves competitive performance on standard benchmarks, while it significantly outperforms them in settings with extreme 3D poses and viewpoints. Additionally, we show that previous reconstruction methods frequently violate joint angle limits, leading to unnatural rotations. In contrast, our approach leverages the biomechanically plausible degrees of freedom making more realistic joint rotation estimates. We validate our approach across multiple human pose estimation benchmarks. We make the code, models and data available at: https://isshikihugh.github.io/HSMR/
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