The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to
predict human joint coordinates in 3D space. Despite recent advancements in
deep learning-based methods, they mostly ignore the capability of coupling
accessible texts and naturally feasible knowledge of humans, missing out on
valuable implicit supervision to guide the 3D HPE task. Moreover, previous
efforts often study this task from the perspective of the whole human body,
neglecting fine-grained guidance hidden in different body parts. To this end,
we present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model
for 3D HPE, named \textbf{FinePOSE}. It consists of three core blocks enhancing
the reverse process of the diffusion model: (1) Fine-grained Part-aware Prompt
learning (FPP) block constructs fine-grained part-aware prompts via coupling
accessible texts and naturally feasible knowledge of body parts with learnable
prompts to model implicit guidance. (2) Fine-grained Prompt-pose Communication
(FPC) block establishes fine-grained communications between learned part-aware
prompts and poses to improve the denoising quality. (3) Prompt-driven Timestamp
Stylization (PTS) block integrates learned prompt embedding and temporal
information related to the noise level to enable adaptive adjustment at each
denoising step. Extensive experiments on public single-human pose estimation
datasets show that FinePOSE outperforms state-of-the-art methods. We further
extend FinePOSE to multi-human pose estimation. Achieving 34.3mm average MPJPE
on the EgoHumans dataset demonstrates the potential of FinePOSE to deal with
complex multi-human scenarios. Code is available at
this https URL

3D 人体姿势估计任务使用 2D 图像或视频预测 3D 空间中的人体关节坐标。本论文提出了一种基于扩散模型的 Fine-Grained Prompt-Driven Denoiser（FinePOSE）用于 3D 人体姿势估计，通过构建细粒度的部分感知提示、建立精细的沟通以及整合动态信息来提高去噪质量，并在公开数据集上展开了广泛实验，显示出 FinePOSE 在单人姿势估计以及多人复杂场景中的潜力。

FinePOSE: 经控制的高精度 3D 人体姿势估计

FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via  Diffusion Models

Its numerous applications make multi-human 3D pose estimation a remarkably
impactful area of research. Nevertheless, assuming a multiple-view system
composed of several regular RGB cameras, 3D multi-pose estimation presents
several challenges. First of all, each person must be uniquely identified in
the different views to separate the 2D information provided by the cameras.
Secondly, the 3D pose estimation process from the multi-view 2D information of
each person must be robust against noise and potential occlusions in the
scenario. In this work, we address these two challenges with the help of deep
learning. Specifically, we present a model based on Graph Neural Networks
capable of predicting the cross-view correspondence of the people in the
scenario along with a Multilayer Perceptron that takes the 2D points to yield
the 3D poses of each person. These two models are trained in a self-supervised
manner, thus avoiding the need for large datasets with 3D annotations.

本文研究多人姿态估计问题，提出了一种基于多视图系统和深度学习的方法，通过使用图神经网络模型预测场景中不同人之间的视角对应关系，并使用多层感知器模型将 2D 点转换为每个人的 3D 姿态。该模型采用自监督学习方式进行训练，从而避免了对大规模 3D 数据集的依赖。