Existing deep learning-based human mesh reconstruction approaches have a
tendency to build larger networks in order to achieve higher accuracy.
Computational complexity and model size are often neglected, despite being key
characteristics for practical use of human mesh reconstruction models (e.g.
virtual try-on systems). In this paper, we present GTRS, a lightweight
pose-based method that can reconstruct human mesh from 2D human pose. We
propose a pose analysis module that uses graph transformers to exploit
structured and implicit joint correlations, and a mesh regression module that
combines the extracted pose feature with the mesh template to reconstruct the
final human mesh. We demonstrate the efficiency and generalization of GTRS by
extensive evaluations on the Human3.6M and 3DPW datasets. In particular, GTRS
achieves better accuracy than the SOTA pose-based method Pose2Mesh while only
using 10.2% of the parameters (Params) and 2.5% of the FLOPs on the challenging
in-the-wild 3DPW dataset. Code will be publicly available.

本文提出了一种基于姿势的轻量级方法 GTRS，它可以从 2D 人类姿态重构人类网格。该方法利用图变换器来提取结构化和隐式的关节相关性以及网格模板来重构人类网格，从而大大提高了模型的效率和泛化性能。在 Human3.6M 和 3DPW 数据集上进行广泛的评估，GTRS 方法在仅使用 10.2％ 的参数和 2.5％ 的 FLOPs 的同时，比 SOTA 基于姿态的 Pose2Mesh 方法实现了更好的准确性。

一个轻量级的图变换网络用于从 2D 人体姿势重建人体网格

A Lightweight Graph Transformer Network for Human Mesh Reconstruction  from 2D Human Pose

Most current action recognition methods heavily rely on appearance
information by taking an RGB sequence of entire image regions as input. While
being effective in exploiting contextual information around humans, e.g., human
appearance and scene category, they are easily fooled by out-of-context action
videos where the contexts do not exactly match with target actions. In
contrast, pose-based methods, which take a sequence of human skeletons only as
input, suffer from inaccurate pose estimation or ambiguity of human pose per
se. Integrating these two approaches has turned out to be non-trivial; training
a model with both appearance and pose ends up with a strong bias towards
appearance and does not generalize well to unseen videos. To address this
problem, we propose to learn pose-driven feature integration that dynamically
combines appearance and pose streams by observing pose features on the fly. The
main idea is to let the pose stream decide how much and which appearance
information is used in integration based on whether the given pose information
is reliable or not. We show that the proposed IntegralAction achieves highly
robust performance across in-context and out-of-context action video datasets.
The codes are available in this https URL

该研究提出了一种动作识别方法，通过动态组合外貌信息和姿势信息，实现在情境内外的动作视频数据集上高度鲁棒的性能表现。

IntegralAction: 基于姿态驱动的特征集成，实现视频中人类行为的稳健识别

IntegralAction: Pose-driven Feature Integration for Robust Human Action  Recognition in Videos

Vision-based sign language recognition aims at helping deaf people to
communicate with others. However, most existing sign language datasets are
limited to a small number of words. Due to the limited vocabulary size, models
learned from those datasets cannot be applied in practice. In this paper, we
introduce a new large-scale Word-Level American Sign Language (WLASL) video
dataset, containing more than 2000 words performed by over 100 signers. This
dataset will be made publicly available to the research community. To our
knowledge, it is by far the largest public ASL dataset to facilitate word-level
sign recognition research.
Based on this new large-scale dataset, we are able to experiment with several
deep learning methods for word-level sign recognition and evaluate their
performances in large scale scenarios. Specifically we implement and compare
two different models,i.e., (i) holistic visual appearance-based approach, and
(ii) 2D human pose based approach. Both models are valuable baselines that will
benefit the community for method benchmarking. Moreover, we also propose a
novel pose-based temporal graph convolution networks (Pose-TGCN) that models
spatial and temporal dependencies in human pose trajectories simultaneously,
which has further boosted the performance of the pose-based method. Our results
show that pose-based and appearance-based models achieve comparable
performances up to 66% at top-10 accuracy on 2,000 words/glosses, demonstrating
the validity and challenges of our dataset. Our dataset and baseline deep
models are available at https://dxli94.github.io/WLASL/.

本文介绍了一个新的大规模美国手语单词语义（WLASL）数据集，并实现了基于外观和人体姿态的 2 个模型，以及提出了一种新的基于姿态的时态图卷积网络（Pose-TGCN）方法，同时模拟人体姿态轨迹中的空间和时间依赖关系，为手语识别研究提供了一个有价值的基准实验平台。