Acquiring contact patterns between hands and nonrigid objects is a common
concern in the vision and robotics community. However, existing learning-based
methods focus more on contact with rigid ones from monocular images. When
adopting them for nonrigid contact, a major problem is that the existing
contact representation is restricted by the geometry of the object.
Consequently, contact neighborhoods are stored in an unordered manner and
contact features are difficult to align with image cues. At the core of our
approach lies a novel hand-object contact representation called RUPs (Region
Unwrapping Profiles), which unwrap the roughly estimated hand-object surfaces
as multiple high-resolution 2D regional profiles. The region grouping strategy
is consistent with the hand kinematic bone division because they are the
primitive initiators for a composite contact pattern. Based on this
representation, our Regional Unwrapping Transformer (RUFormer) learns the
correlation priors across regions from monocular inputs and predicts
corresponding contact and deformed transformations. Our experiments demonstrate
that the proposed framework can robustly estimate the deformed degrees and
deformed transformations, which makes it suitable for both nonrigid and rigid
contact.

通过 RUPs 和 RUFormer 方法，本研究提出了一种新颖的手 - 物体接触表示方法，可以有效地预测非刚性接触中的形变程度和变形转换，适用于刚性和非刚性接触模式。

基于区域展开变换器的非刚体物体接触估计

Nonrigid Object Contact Estimation With Regional Unwrapping Transformer

Contacts between patients, patients and health care workers (HCWs) and among
HCWs represent one of the important routes of transmission of hospital-acquired
infections (HAI). A detailed description and quantification of contacts in
hospitals provides key information for HAIs epidemiology and for the design and
validation of control measures. We used wearable sensors to detect close-range
interactions ("contacts") between individuals in the geriatric unit of a
university hospital. Contact events were measured with a spatial resolution of
about 1.5 meters and a temporal resolution of 20 seconds. The study included 46
HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were
recorded. The number and duration of contacts varied between mornings,
afternoons and nights, and contact matrices describing the mixing patterns
between HCW and patients were built for each time period. Contact patterns were
qualitatively similar from one day to the next. 38% of the contacts occurred
between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts
including at least one patient, suggesting a population of individuals who
could potentially act as super-spreaders. Wearable sensors represent a novel
tool for the measurement of contact patterns in hospitals. The collected data
provides information on important aspects that impact the spreading patterns of
infectious diseases, such as the strong heterogeneity of contact numbers and
durations across individuals, the variability in the number of contacts during
a day, and the fraction of repeated contacts across days. This variability is
associated with a marked statistical stability of contact and mixing patterns
across days. Our results highlight the need for such measurement efforts in
order to correctly inform mathematical models of HAIs and use them to inform
the design and evaluation of prevention strategies.

病人、医护人员之间的接触模式是医院感染传播的重要途径，此研究利用可穿戴传感器监测接触事件，发现不同时间段医患接触次数和持续时间的差异，部分医护人员的接触次数较多，这些数据可为感染疾病传播模式的数学模型提供关键信息，推进制定有效的预防策略。