3D hand pose estimation from images has seen considerable interest from the
literature, with new methods improving overall 3D accuracy. One current
challenge is to address hand-to-hand interaction where self-occlusions and
finger articulation pose a significant problem to estimation. Little work has
applied physical constraints that minimize the hand intersections that occur as
a result of noisy estimation. This work addresses the intersection of hands by
exploiting an occupancy network that represents the hand's volume as a
continuous manifold. This allows us to model the probability distribution of
points being inside a hand. We designed an intersection loss function to
minimize the likelihood of hand-to-point intersections. Moreover, we propose a
new hand mesh parameterization that is superior to the commonly used MANO model
in many respects including lower mesh complexity, underlying 3D skeleton
extraction, watertightness, etc. On the benchmark InterHand2.6M dataset, the
models trained using our intersection loss achieve better results than the
state-of-the-art by significantly decreasing the number of hand intersections
while lowering the mean per-joint positional error. Additionally, we
demonstrate superior performance for 3D hand uplift on Re:InterHand and SMILE
datasets and show reduced hand-to-hand intersections for complex domains such
as sign-language pose estimation.

利用占用网络解决手与手交互带来的干扰问题，降低手的相交数，并提出了一种新的手网格参数化方法，实现更好的性能和更低的误差。

双手胜于一手：通过占位网络解决手与手的相交

Two Hands Are Better Than One: Resolving Hand to Hand Intersections via  Occupancy Networks

Recognizing interactive actions, including hand-to-hand interaction and
human-to-human interaction, has attracted increasing attention for various
applications in the field of video analysis and human-robot interaction.
Considering the success of graph convolution in modeling topology-aware
features from skeleton data, recent methods commonly operate graph convolution
on separate entities and use late fusion for interactive action recognition,
which can barely model the mutual semantic relationships between pairwise
entities. To this end, we propose a mutual excitation graph convolutional
network (me-GCN) by stacking mutual excitation graph convolution (me-GC)
layers. Specifically, me-GC uses a mutual topology excitation module to firstly
extract adjacency matrices from individual entities and then adaptively model
the mutual constraints between them. Moreover, me-GC extends the above idea and
further uses a mutual feature excitation module to extract and merge deep
features from pairwise entities. Compared with graph convolution, our proposed
me-GC gradually learns mutual information in each layer and each stage of graph
convolution operations. Extensive experiments on a challenging hand-to-hand
interaction dataset, i.e., the Assembely101 dataset, and two large-scale
human-to-human interaction datasets, i.e., NTU60-Interaction and
NTU120-Interaction consistently verify the superiority of our proposed method,
which outperforms the state-of-the-art GCN-based and Transformer-based methods.

提出了一种互激励图卷积网络 (me-GCN)，通过堆叠互激励图卷积 (me-GC) 层来识别交互动作，成功地建模了实体间的相互语义关系，实验证明了该方法在手对手交互和人对人交互数据集上的优越性。