3D Human Pose Estimation (HPE) is a one-to-many problem by nature, making it challenging to estimate an accurate 3D pose from a single 2D pose. Some prior works have attempted to tackle this problem by using a conditional generative network. They generate 3D poses from a given 2D pose with noises from a standard Gaussian distribution, while the depth distribution is dependent on each posture and more complex than the standard Gaussian distribution. This may lead to inaccurate distribution learning. In this paper, we propose a probabilistic framework called ProPose to address this issue. ProPose employs Pose Instance-Level Gaussian Distribution (PILGD) derived from 3D pose-based self-representation learning to obtain reliable distribution which is able to address pose-dependent depth distribution. To access this PILGD, we utilize normalizing flow, which learns a mapping function between the PILGD and a 2D Pose-Adaptive Gaussian Distribution (PAGD). This converts the problem of directly estimating 3D poses from 2D poses to a mapping problem between PILGD and PAGD using a normalizing flow. Extensive experiments show the advantages of utilizing the PILGD and PAGD. ProPose achieves comparable performances to previous state-of-the-art probabilistic methods in a multi-hypothesis setting. Notably, ProPose in a single-hypothesis setting demonstrates comparable generalization ability to existing state-of-the-art deterministic methods.

3D Human Pose Estimation (HPE) is a fundamental task in the computer vision. Generalization in 3D HPE task is crucial due to the need for robustness across diverse environments and datasets. Existing methods often focus on learning relationships between joints to enhance the generalization capability, but the role of the loss landscape, which is closely tied to generalization, remains underexplored. In this paper, we empirically visualize the loss landscape of the 3D HPE task, revealing its complexity and the challenges it poses for optimization. To address this, we first introduce a simple adaptive scaling mechanism that smooths the loss landscape. We further observe that different solutions on this smoothed loss landscape exhibit varying generalization behaviors. Based on this insight, we propose an efficient ensemble approach that combines diverse solutions on the smooth loss landscape induced by our adaptive scaling mechanism. Extensive experimental results demonstrate that our approach improves the generalization capability of 3D HPE models, and can be easily applied, regardless of model architecture, with consistent performance gains.