面向未见领域的自体视觉三维手姿态估计
Towards Egocentric 3D Hand Pose Estimation in Unseen Domains
AI总结：
V-HPOT通过虚拟相机空间和自监督优化提升跨领域三维手姿态估计性能，减少71%的平均姿态误差。
摘要：
我们提出了V-HPOT，一种新的方法，用于提高从自体图像中进行跨领域三维手姿态估计的性能。最先进的方法在训练和测试在同一领域时表现出色。然而，由于训练数据有限和深度感知的问题，它们在新环境中泛化能力差，容易过拟合特定相机内参。我们的方法通过在虚拟相机空间中估计关键点的z坐标，将其归一化为焦距和图像大小，从而实现相机无关的深度预测。我们进一步利用这种对相机内参的不变性，提出了一种自监督的测试时优化策略，通过在推理过程中应用预测与空间尺度变换后的手姿态之间的3D一致性损失，使模型能够适应目标领域特性，而无需真实标注。V-HPOT在跨领域场景中显著提高了三维手姿态估计性能，在H2O数据集上平均姿态误差减少了71%，在AssemblyHands数据集上减少了41%。与最先进的方法相比，V-HPOT在所有数据集上均优于所有单阶段方法，并与两种阶段方法竞争，尽管其需要的数据量大约是x3.5到x14少。
英文摘要：
We present V-HPOT, a novel approach for improving the cross-domain performance of 3D hand pose estimation from egocentric images across diverse, unseen domains. State-of-the-art methods demonstrate strong performance when trained and tested within the same domain. However, they struggle to generalise to new environments due to limited training data and depth perception -- overfitting to specific camera intrinsics. Our method addresses this by estimating keypoint z-coordinates in a virtual camera space, normalised by focal length and image size, enabling camera-agnostic depth prediction. We further leverage this invariance to camera intrinsics to propose a self-supervised test-time optimisation strategy that refines the model's depth perception during inference. This is achieved by applying a 3D consistency loss between predicted and in-space scale-transformed hand poses, allowing the model to adapt to target domain characteristics without requiring ground truth annotations. V-HPOT significantly improves 3D hand pose estimation performance in cross-domain scenarios, achieving a 71% reduction in mean pose error on the H2O dataset and a 41% reduction on the AssemblyHands dataset. Compared to state-of-the-art methods, V-HPOT outperforms all single-stage approaches across all datasets and competes closely with two-stage methods, despite needing approximately x3.5 to x14 less 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。
