Inferring 3D human pose from 2D images is a challenging and long-standing
problem in the field of computer vision with many applications including motion
capture, virtual reality, surveillance or gait analysis for sports and
medicine. We present preliminary results for a method to estimate 3D pose from
2D video containing a single person and a static background without the need
for any manual landmark annotations. We achieve this by formulating a simple
yet effective self-supervision task: our model is required to reconstruct a
random frame of a video given a frame from another timepoint and a rendered
image of a transformed human shape template. Crucially for optimisation, our
ray casting based rendering pipeline is fully differentiable, enabling end to
end training solely based on the reconstruction task.

本文介绍了一种利用自监督学习估算不需要人工标注的静态背景下单个人物的 2D 视频帧生成 3D 人体姿势的方法。作者采用射线投射技术进行人体模板的渲染，使神经网络可以对渲染结果进行优化，从而获得了令人满意的结果。

通过神经渲染在静态视频中进行自监督的三维人体姿势估计

Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering

Localization is an essential component for autonomous robots. A
well-established localization approach combines ray casting with a particle
filter, leading to a computationally expensive algorithm that is difficult to
run on resource-constrained mobile robots. We present a novel data structure
called the Compressed Directional Distance Transform for accelerating ray
casting in two dimensional occupancy grid maps. Our approach allows online map
updates, and near constant time ray casting performance for a fixed size map,
in contrast with other methods which exhibit poor worst case performance. Our
experimental results show that the proposed algorithm approximates the
performance characteristics of reading from a three dimensional lookup table of
ray cast solutions while requiring two orders of magnitude less memory and
precomputation. This results in a particle filter algorithm which can maintain
2500 particles with 61 ray casts per particle at 40Hz, using a single CPU
thread onboard a mobile robot.

本文介绍了一种名为压缩方向距离变换 (Compressed Directional Distance Transform) 的数据结构，通过将其与粒子滤波器相结合，加速了二维占用栅格地图上的射线投影。实验结果表明，该算法可以在固定地图大小下实现近似的三维查找表效果，但内存和预计算需求仅为原来的百分之二。因此，该算法可以在单个 CPU 线程上运行，使用粒子滤波器维护 2500 个粒子，每个粒子执行 61 次射线投影，实现 40Hz 工作频率。所提出的算法在资源有限的移动机器人上的应用具有实际意义。