Automatic markerless estimation of infant posture and motion from ordinary
videos carries great potential for movement studies "in the wild", facilitating
understanding of motor development and massively increasing the chances of
early diagnosis of disorders. There is rapid development of human pose
estimation methods in computer vision thanks to advances in deep learning and
machine learning. However, these methods are trained on datasets featuring
adults in different contexts. This work tests and compares seven popular
methods (AlphaPose, DeepLabCut/DeeperCut, Detectron2, HRNet,
MediaPipe/BlazePose, OpenPose, and ViTPose) on videos of infants in supine
position. Surprisingly, all methods except DeepLabCut and MediaPipe have
competitive performance without additional finetuning, with ViTPose performing
best. Next to standard performance metrics (object keypoint similarity, average
precision and recall), we introduce errors expressed in the neck-mid-hip ratio
and additionally study missed and redundant detections and the reliability of
the internal confidence ratings of the different methods, which are relevant
for downstream tasks. Among the networks with competitive performance, only
AlphaPose could run close to real time (27 fps) on our machine. We provide
documented Docker containers or instructions for all the methods we used, our
analysis scripts, and processed data at this https URL
and this https URL

通过比较七种不同的姿势估计方法，对婴儿在仰卧位的视频进行分析，发现除了 DeepLabCut 和 MediaPipe 外，其他方法在无需微调的情况下都表现出竞争力，其中 ViTPose 表现最佳。同时，引入颈中臀比例的错误和其他误测、多余检测以及不同方法的内部置信度评分可靠性的研究，这些都对下游任务有重要意义。

视频中自动婴儿二维姿势估计：比较七种深度神经网络方法

Automatic infant 2D pose estimation from videos: comparing seven deep  neural network methods

This paper introduces a novel, markerless, step-by-step, in-situ 3D Augmented
Reality (AR) instruction method and its application - BRICKxAR (Multi 3D
Models/M3D) - for small parts assembly. BRICKxAR (M3D) realistically visualizes
rendered 3D assembly parts at the assembly location of the physical assembly
model (Figure 1). The user controls the assembly process through a user
interface. BRICKxAR (M3D) utilizes deep learning-trained 3D model-based
registration. Object recognition and tracking become challenging as the
assembly model updates at each step. Additionally, not every part in a 3D
assembly may be visible to the camera during the assembly. BRICKxAR (M3D)
combines multiple assembly phases with a step count to address these
challenges. Thus, using fewer phases simplifies the complex assembly process
while step count facilitates accurate object recognition and precise
visualization of each step. A testing and heuristic evaluation of the BRICKxAR
(M3D) prototype and qualitative analysis were conducted with users and experts
in visualization and human-computer interaction. Providing robust 3D AR
instructions and allowing the handling of the assembly model, BRICKxAR (M3D)
has the potential to be used at different scales ranging from manufacturing
assembly to construction.

介绍一种新的无标记、分步、现场三维增强现实（AR）指导方法及其应用 - BRICKxAR（Multi 3D Models/M3D），用于小零件组装。通过使用深度学习训练的三维模型注册，BRICKxAR（M3D）能够在物理组装模型的组装位置逼真地可视化渲染的三维组装零件，并通过用户界面控制组装流程，同时结合多个组装阶段和步骤计数的方式来解决物体识别和跟踪的难题，从而简化复杂的组装过程并实现准确的物体识别和精确的可视化展示。通过对 BRICKxAR（M3D）的原型进行测试和启发式评估，并与可视化和人机交互领域的用户和专家进行定性分析，该方法具有提供强大的三维增强现实指导和处理组装模型的潜力，可应用于从制造组装到建筑施工等不同规模的领域。

基于多个 3D 模型的注册的增强现实（AR）装配指导

Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions  for Assembly

Hand-eye calibration is a critical task in robotics, as it directly affects
the efficacy of critical operations such as manipulation and grasping.
Traditional methods for achieving this objective necessitate the careful design
of joint poses and the use of specialized calibration markers, while most
recent learning-based approaches using solely pose regression are limited in
their abilities to diagnose inaccuracies. In this work, we introduce a new
approach to hand-eye calibration called EasyHeC, which is markerless,
white-box, and offers comprehensive coverage of positioning accuracy across the
entire robot configuration space. We introduce two key technologies:
differentiable rendering-based camera pose optimization and consistency-based
joint space exploration, which enables accurate end-to-end optimization of the
calibration process and eliminates the need for the laborious manual design of
robot joint poses. Our evaluation demonstrates superior performance in
synthetic and real-world datasets, enhancing downstream manipulation tasks by
providing precise camera poses for locating and interacting with objects. The
code is available at the project page: this https URL

EasyHeC 是一种新的手眼标定方法，采用无标记，白盒的方法，通过可微分渲染的摄像头姿态优化和基于一致性的关节空间探索，能够准确优化标定过程，消除手动设计机器人关节姿势的繁琐过程。通过在合成和真实的数据集上进行评估，EasyHeC 表现出优异的性能，提供精确的摄像机姿态，增强下游的操作任务。