Existing Transformers for monocular 3D human shape and pose estimation
typically have a quadratic computation and memory complexity with respect to
the feature length, which hinders the exploitation of fine-grained information
in high-resolution features that is beneficial for accurate reconstruction. In
this work, we propose an SMPL-based Transformer framework (SMPLer) to address
this issue. SMPLer incorporates two key ingredients: a decoupled attention
operation and an SMPL-based target representation, which allow effective
utilization of high-resolution features in the Transformer. In addition, based
on these two designs, we also introduce several novel modules including a
multi-scale attention and a joint-aware attention to further boost the
reconstruction performance. Extensive experiments demonstrate the effectiveness
of SMPLer against existing 3D human shape and pose estimation methods both
quantitatively and qualitatively. Notably, the proposed algorithm achieves an
MPJPE of 45.2 mm on the Human3.6M dataset, improving upon Mesh Graphormer by
more than 10% with fewer than one-third of the parameters. Code and pretrained
models are available at this https URL

通过引入两个关键元素，即分离的注意力操作和基于 SMPL 的目标表示，本文提出了一种面向 SMPL 的 Transformer 框架（SMPLer），通过有效利用 Transformer 中的高分辨率特征来解决了现有 monocular 3D human shape 和 pose estimation 模型在计算和内存复杂度方面的问题，并通过多个新模块进一步提高重建性能。在实验证明了 SMPLer 在量化和定性方面都比现有的 3D human shape 和 pose estimation 方法效果更好，其中在 Human3.6M 数据集上实现了 45.2 毫米的平均关节定位误差，相较于 Mesh Graphormer 提升了 10% 以上且参数数量不到三分之一。

SMPLer：针对单目 3D 人体形状和姿势估计的 Transformer 模型改进

SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose  Estimation

This paper addresses the problem of monocular 3D human shape and pose
estimation from an RGB image. Despite great progress in this field in terms of
pose prediction accuracy, state-of-the-art methods often predict inaccurate
body shapes. We suggest that this is primarily due to the scarcity of
in-the-wild training data with diverse and accurate body shape labels. Thus, we
propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system
that utilises proxy representations, such as silhouettes and 2D joints, as
inputs to a shape and pose regression neural network, which is trained with
synthetic training data (generated on-the-fly during training using the SMPL
statistical body model) to overcome data scarcity. We bridge the gap between
synthetic training inputs and noisy real inputs, which are predicted by
keypoint detection and segmentation CNNs at test-time, by using data
augmentation and corruption during training. In order to evaluate our approach,
we curate and provide a challenging evaluation dataset for monocular human
shape estimation, Sports Shape and Pose 3D (SSP-3D). It consists of RGB images
of tightly-clothed sports-persons with a variety of body shapes and
corresponding pseudo-ground-truth SMPL shape and pose parameters, obtained via
multi-frame optimisation. We show that STRAPS outperforms other
state-of-the-art methods on SSP-3D in terms of shape prediction accuracy, while
remaining competitive with the state-of-the-art on pose-centric datasets and
metrics.

本文提出了 STRAPS 系统，它利用代理表示法（如轮廓线和 2D 关节点）作为输入，使用合成数据来培训形状和姿态回归神经网络，以克服数据稀缺性问题，并在人体形状估计方面的具有挑战性的 SSP-3D 数据集上表现出色，其精度超过其他最先进的方法。