重建具生物力學精確骨骼的人體模型

Reconstructing Humans with a Biomechanically Accurate Skeleton

March 27, 2025

作者: Yan Xia, Xiaowei Zhou, Etienne Vouga, Qixing Huang, Georgios Pavlakos

cs.AI

摘要

在本論文中，我們提出了一種基於生物力學精確骨架模型，從單一圖像重建三維人體的方法。為實現這一目標，我們訓練了一個以圖像為輸入並估計模型參數的Transformer模型。由於此任務缺乏訓練數據，我們構建了一個流程來為單張圖像生成偽真值模型參數，並實施了一種迭代精煉這些偽標籤的訓練程序。與當前最先進的三維人體網格恢復方法相比，我們的模型在標準基準測試中展現了競爭力，同時在極端三維姿態和視角設置下顯著超越它們。此外，我們指出，先前的重建方法經常違反關節角度限制，導致不自然的旋轉。相比之下，我們的方法利用了生物力學上合理的自由度，從而做出更為真實的關節旋轉估計。我們在多個人體姿態估計基準上驗證了我們的方法。我們將代碼、模型及數據公開於：https://isshikihugh.github.io/HSMR/。

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/