We present imGHUM, the first holistic generative model of 3D human shape and
articulated pose, represented as a signed distance function. In contrast to
prior work, we model the full human body implicitly as a function
zero-level-set and without the use of an explicit template mesh. We propose a
novel network architecture and a learning paradigm, which make it possible to
learn a detailed implicit generative model of human pose, shape, and semantics,
on par with state-of-the-art mesh-based models. Our model features desired
detail for human models, such as articulated pose including hand motion and
facial expressions, a broad spectrum of shape variations, and can be queried at
arbitrary resolutions and spatial locations. Additionally, our model has
attached spatial semantics making it straightforward to establish
correspondences between different shape instances, thus enabling applications
that are difficult to tackle using classical implicit representations. In
extensive experiments, we demonstrate the model accuracy and its applicability
to current research problems.

imGHUM 是一个基于有效隐式模型，能够生成细节丰富人体模型的生成模型，实现了全人体的大规模形态与姿态的查询。

imGHUM: 三维人体形状和关节姿态的隐式生成模型

imGHUM: Implicit Generative Models of 3D Human Shape and Articulated  Pose

We propose a new spatio-temporal attention based mechanism for human action
recognition able to automatically attend to the hands most involved into the
studied action and detect the most discriminative moments in an action.
Attention is handled in a recurrent manner employing Recurrent Neural Network
(RNN) and is fully-differentiable. In contrast to standard soft-attention based
mechanisms, our approach does not use the hidden RNN state as input to the
attention model. Instead, attention distributions are extracted using external
information: human articulated pose. We performed an extensive ablation study
to show the strengths of this approach and we particularly studied the
conditioning aspect of the attention mechanism. We evaluate the method on the
largest currently available human action recognition dataset, NTU-RGB+D, and
report state-of-the-art results. Other advantages of our model are certain
aspects of explanability, as the spatial and temporal attention distributions
at test time allow to study and verify on which parts of the input data the
method focuses.

本研究提出一种基于时空注意力机制的人体动作识别方法，采用外部信息（人的姿态）提取注意力分布，采用 RNN 实现注意力的递归处理，以实现自动关注动作中最活跃的手部，并检测最具区分度的动作要素，并在 NTU-RGB + D 数据集上获得最先进的结果。