蹦床体操中的人体姿态估计：利用新合成数据集提高性能
Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses
AI总结：
本文通过合成蹦床姿态数据集STP提升姿态估计性能，采用ViTPose模型进行微调，在2D和3D姿态估计中取得显著改进，3D精度提升12.5mm。
摘要：
蹦床体操涉及极端人体姿态和不常见视角，当前最先进的姿态估计模型在此类数据上表现欠佳。本文通过在合成蹦床姿态数据集（STP）上微调姿态估计模型来解决此问题。STP由蹦床动作的运动捕捉记录生成。我们开发了一条流程，将噪声的运动捕捉数据拟合到参数化人体模型，然后生成多视角逼真图像。我们使用此数据对ViTPose模型进行微调，并在真实多视角蹦床图像上测试。所得模型在2D上实现了准确率的提升，这转化为3D三角化性能的改进。在2D上，我们在这种具有挑战性的数据上取得了最先进的结果，缩小了常见姿态和极端姿态之间的性能差距。在3D中，我们最佳模型将MPJPE减少了12.5mm，相比预训练的ViTPose模型提高了19.6%。
英文摘要：
Trampoline gymnastics involves extreme human poses and uncommon viewpoints, on which state-of-the art pose estimation models tend to under-perform. We demonstrate that this problem can be addressed by fine-tuning a pose estimation model on a combination of real extreme poses and domain-specific synthetic poses (STP). We generate STP from motion capture recordings of trampoline routines. We propose a pipeline to fit noisy motion capture data to a parametric human model, then generate multi-view realistic images with high-fidelity keypoint labels. The fine-tuned ViTPose model tested on real multi-view images exhibits accuracy improvements in 2D which translate to improved 3D triangulation. In 2D, we obtain a performance similar to state-of-the-art models on the MS COCO validation set while evaluating on significantly more challenging data, bridging the performance gap between common and extreme poses. In 3D, we reduce the MPJPE by 46.1 mm with our best model, which represents an improvement of 42.7% compared to the pretrained ViTPose model. Our code and data are available at https://github.com/VisionICLab/trampoline_syn_data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
