Recent advances in image-based 3D human shape estimation have been driven by
the significant improvement in representation power afforded by deep neural
networks. Although current approaches have demonstrated the potential in real
world settings, they still fail to produce reconstructions with the level of
detail often present in the input images. We argue that this limitation stems
primarily form two conflicting requirements; accurate predictions require large
context, but precise predictions require high resolution. Due to memory
limitations in current hardware, previous approaches tend to take low
resolution images as input to cover large spatial context, and produce less
precise (or low resolution) 3D estimates as a result. We address this
limitation by formulating a multi-level architecture that is end-to-end
trainable. A coarse level observes the whole image at lower resolution and
focuses on holistic reasoning. This provides context to an fine level which
estimates highly detailed geometry by observing higher-resolution images. We
demonstrate that our approach significantly outperforms existing
state-of-the-art techniques on single image human shape reconstruction by fully
leveraging 1k-resolution input images.

该研究论文通过多层次结构体系，从高分辨率的图像中提取细节信息、同时观察到低分辨率图像的整体信息，借助深度神经网络尝试解决现有方法在人体三维建模方面存在的精度与准确预测之间的矛盾，实验结果在单幅图像三维建模领域中，显著超越了现有的最先进技术。

PIFuHD: 面向高分辨率三维人体数字化的多级像素对齐隐式函数

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution  3D Human Digitization

Experience reuse is key to sample-efficient reinforcement learning. One of
the critical issues is how the experience is represented and stored.
Previously, the experience can be stored in the forms of features, individual
models, and the average model, each lying at a different granularity. However,
new tasks may require experience across multiple granularities. In this paper,
we propose the policy residual representation (PRR) network, which can extract
and store multiple levels of experience. PRR network is trained on a set of
tasks with a multi-level architecture, where a module in each level corresponds
to a subset of the tasks. Therefore, the PRR network represents the experience
in a spectrum-like way. When training on a new task, PRR can provide different
levels of experience for accelerating the learning. We experiment with the PRR
network on a set of grid world navigation tasks, locomotion tasks, and fighting
tasks in a video game. The results show that the PRR network leads to better
reuse of experience and thus outperforms some state-of-the-art approaches.

本文研究了经验重用在强化学习中的重要性，针对不同粒度的任务设计了一种策略残差表示网络（PRR），实现了多层级经验的提取和存储，通过在一组不同类型的任务上实验验证了它的有效性。