3D human pose estimation and mesh recovery have attracted widespread research
interest in many areas, such as computer vision, autonomous driving, and
robotics. Deep learning on 3D human pose estimation and mesh recovery has
recently thrived, with numerous methods proposed to address different problems
in this area. In this paper, to stimulate future research, we present a
comprehensive review of recent progress over the past five years in deep
learning methods for this area by delving into over 200 references. To the best
of our knowledge, this survey is arguably the first to comprehensively cover
deep learning methods for 3D human pose estimation, including both
single-person and multi-person approaches, as well as human mesh recovery,
encompassing methods based on explicit models and implicit representations. We
also present comparative results on several publicly available datasets,
together with insightful observations and inspiring future research directions.
A regularly updated project page can be found at
this https URL

本文通过研究 200 多篇论文，全面回顾了过去五年中 3D 人体姿态估计和网格恢复领域的深度学习方法进展，包括单人和多人方法，以及基于显式模型和隐式表示的人体网格恢复方法，并提供了公开数据集上的比较结果，以及深入观察和激发未来研究方向。

深度学习用于 3D 人体姿势估计和网格恢复的综述

Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey

Recovering whole-body mesh by inferring the abstract pose and shape
parameters from visual content can obtain 3D bodies with realistic structures.
However, the inferring process is highly non-linear and suffers from image-mesh
misalignment, resulting in inaccurate reconstruction. In contrast, 3D keypoint
estimation methods utilize the volumetric representation to achieve pixel-level
accuracy but may predict unrealistic body structures. To address these issues,
this paper presents a novel hybrid inverse kinematics solution, HybrIK, that
integrates the merits of 3D keypoint estimation and body mesh recovery in a
unified framework. HybrIK directly transforms accurate 3D joints to body-part
rotations via twist-and-swing decomposition. The swing rotations are
analytically solved with 3D joints, while the twist rotations are derived from
visual cues through neural networks. To capture comprehensive whole-body
details, we further develop a holistic framework, HybrIK-X, which enhances
HybrIK with articulated hands and an expressive face. HybrIK-X is fast and
accurate by solving the whole-body pose with a one-stage model. Experiments
demonstrate that HybrIK and HybrIK-X preserve both the accuracy of 3D joints
and the realistic structure of the parametric human model, leading to
pixel-aligned whole-body mesh recovery. The proposed method significantly
surpasses the state-of-the-art methods on various benchmarks for body-only,
hand-only, and whole-body scenarios. Code and results can be found at
this https URL

本研究提出了一个新型的 HybrIK 反向运动学解决方案，并使用扭转摆动分解将准确的三维关节点直接转换为身体部位旋转。通过混合 3D 关键点估计方法和人体网格恢复的方法，将 3D 关键点的精确性和人体模型的真实结构保留下来，为像素对齐的整体人体网格恢复提供了新思路。

HybrIK-X: 基于混合解析神经网络的全身网格恢复逆运动学

HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body  Mesh Recovery

This paper focuses on the problem of 3D human reconstruction from 2D
evidence. Although this is an inherently ambiguous problem, the majority of
recent works avoid the uncertainty modeling and typically regress a single
estimate for a given input. In contrast to that, in this work, we propose to
embrace the reconstruction ambiguity and we recast the problem as learning a
mapping from the input to a distribution of plausible 3D poses. Our approach is
based on the normalizing flows model and offers a series of advantages. For
conventional applications, where a single 3D estimate is required, our
formulation allows for efficient mode computation. Using the mode leads to
performance that is comparable with the state of the art among deterministic
unimodal regression models. Simultaneously, since we have access to the
likelihood of each sample, we demonstrate that our model is useful in a series
of downstream tasks, where we leverage the probabilistic nature of the
prediction as a tool for more accurate estimation. These tasks include
reconstruction from multiple uncalibrated views, as well as human model
fitting, where our model acts as a powerful image-based prior for mesh
recovery. Our results validate the importance of probabilistic modeling, and
indicate state-of-the-art performance across a variety of settings. Code and
models are available at: this https URL

本文提出一种基于正常流模型的方法，利用概率建模的思想，从多个视角的 2D 图像集合中重建 3D 人体姿势。该方法在人体姿势重建、模拟及建模上都取得了较好的效果，且可通过提供多种样本取得更为准确的估计。