Hand-Object Interactions (HOIs) are conditioned on spatial and temporal
contexts like surrounding objects, pre- vious actions, and future intents (for
example, grasping and handover actions vary greatly based on objects proximity
and trajectory obstruction). However, existing datasets for 4D HOI (3D HOI over
time) are limited to one subject inter- acting with one object only. This
restricts the generalization of learning-based HOI methods trained on those
datasets. We introduce ADL4D, a dataset of up to two subjects inter- acting
with different sets of objects performing Activities of Daily Living (ADL) like
breakfast or lunch preparation ac- tivities. The transition between multiple
objects to complete a certain task over time introduces a unique context
lacking in existing datasets. Our dataset consists of 75 sequences with a total
of 1.1M RGB-D frames, hand and object poses, and per-hand fine-grained action
annotations. We develop an automatic system for multi-view multi-hand 3D pose
an- notation capable of tracking hand poses over time. We inte- grate and test
it against publicly available datasets. Finally, we evaluate our dataset on the
tasks of Hand Mesh Recov- ery (HMR) and Hand Action Segmentation (HAS).

ADL4D 是一个包含最多两个主体与不同物体进行日常生活活动的数据集，用于在手 - 物体交互的任务中提供独特的上下文。该研究还介绍了一个能够跟踪时间上的手部姿势的多视角多手 3D 姿势标注的自动化系统，并在 Hand Mesh Recovery（HMR）和 Hand Action Segmentation（HAS）任务上对数据集进行了评估。

ADL4D：针对 4D 日常生活活动的上下文丰富数据集

ADL4D: Towards A Contextually Rich Dataset for 4D Activities of Daily  Living

Existing Visual Object Tracking (VOT) only takes the target area in the first
frame as a template. This causes tracking to inevitably fail in fast-changing
and crowded scenes, as it cannot account for changes in object appearance
between frames. To this end, we revamped the tracking framework with
Progressive Context Encoding Transformer Tracker (ProContEXT), which coherently
exploits spatial and temporal contexts to predict object motion trajectories.
Specifically, ProContEXT leverages a context-aware self-attention module to
encode the spatial and temporal context, refining and updating the multi-scale
static and dynamic templates to progressively perform accurate tracking. It
explores the complementary between spatial and temporal context, raising a new
pathway to multi-context modeling for transformer-based trackers. In addition,
ProContEXT revised the token pruning technique to reduce computational
complexity. Extensive experiments on popular benchmark datasets such as GOT-10k
and TrackingNet demonstrate that the proposed ProContEXT achieves
state-of-the-art performance.

本研究提出了一种基于 Progressive Context Encoding 的可变模板追踪，利用空间和时间上下文预测物体运动轨迹，同时探索了多种上下文的建模方法并在多个基准数据集上获得了最先进的性能。