In recent years, point cloud perception tasks have been garnering increasing
attention. This paper presents the first attempt to estimate 3D human body mesh
from sparse LiDAR point clouds. We found that the major challenge in estimating
human pose and mesh from point clouds lies in the sparsity, noise, and
incompletion of LiDAR point clouds. Facing these challenges, we propose an
effective sparse-to-dense reconstruction scheme to reconstruct 3D human mesh.
This involves estimating a sparse representation of a human (3D human pose) and
gradually reconstructing the body mesh. To better leverage the 3D structural
information of point clouds, we employ a cascaded graph transformer
(graphormer) to introduce point cloud features during sparse-to-dense
reconstruction. Experimental results on three publicly available databases
demonstrate the effectiveness of the proposed approach. Code:
this https URL

本文首次尝试从稀疏的 LiDAR 点云中估计 3D 人体网格，提出了一种有效的稀疏到稠密重建方案，并通过引入图形变换器来更好地利用点云的 3D 结构信息，实验证明了该方法的有效性。

LiDAR-HMR: LiDAR 中的 3D 人体网格恢复

LiDAR-HMR: 3D Human Mesh Recovery from LiDAR

This work focuses on the problem of reconstructing a 3D human body mesh from
a given 2D image. Despite the inherent ambiguity of the task of human mesh
recovery, most existing works have adopted a method of regressing a single
output. In contrast, we propose a generative approach framework, called
"Diffusion-based Human Mesh Recovery (Diff-HMR)" that takes advantage of the
denoising diffusion process to account for multiple plausible outcomes. During
the training phase, the SMPL parameters are diffused from ground-truth
parameters to random distribution, and Diff-HMR learns the reverse process of
this diffusion. In the inference phase, the model progressively refines the
given random SMPL parameters into the corresponding parameters that align with
the input image. Diff-HMR, being a generative approach, is capable of
generating diverse results for the same input image as the input noise varies.
We conduct validation experiments, and the results demonstrate that the
proposed framework effectively models the inherent ambiguity of the task of
human mesh recovery in a probabilistic manner. The code is available at
this https URL

通过使用基于扩散的人体网格恢复（Diff-HMR）的生成方法框架，本文提出了一种解决从给定 2D 图像重建 3D 人体网格的问题的方法，来解决任务的固有模糊度。验证实验表明，该框架能够以概率的方式有效模拟任务的固有模糊度。

基于扩散模型的概率人体网格恢复的生成方法

Generative Approach for Probabilistic Human Mesh Recovery using  Diffusion Models

We describe an end-to-end method for recovering 3D human body mesh from
single images and monocular videos. Different from the existing methods try to
obtain all the complex 3D pose, shape, and camera parameters from one coupling
feature, we propose a skeleton-disentangling based framework, which divides
this task into multi-level spatial and temporal granularity in a decoupling
manner. In spatial, we propose an effective and pluggable "disentangling the
skeleton from the details" (DSD) module. It reduces the complexity and
decouples the skeleton, which lays a good foundation for temporal modeling. In
temporal, the self-attention based temporal convolution network is proposed to
efficiently exploit the short and long-term temporal cues. Furthermore, an
unsupervised adversarial training strategy, temporal shuffles and order
recovery, is designed to promote the learning of motion dynamics. The proposed
method outperforms the state-of-the-art 3D human mesh recovery methods by 15.4%
MPJPE and 23.8% PA-MPJPE on Human3.6M. State-of-the-art results are also
achieved on the 3D pose in the wild (3DPW) dataset without any fine-tuning.
Especially, ablation studies demonstrate that skeleton-disentangled
representation is crucial for better temporal modeling and generalization.

提出了一种骨架解耦的 3D 人体网格恢复方法，该方法利用自我注意力机制实现多级空间和时间粒度的分离建模，并通过无监督对抗训练策略以及临时混洗和顺序恢复，成功提高了人体网格恢复的精度。