There has been great progress in human 3D mesh recovery and great interest in
learning about the world from consumer video data. Unfortunately current
methods for 3D human mesh recovery work rather poorly on consumer video data,
since on the Internet, unusual camera viewpoints and aggressive truncations are
the norm rather than a rarity. We study this problem and make a number of
contributions to address it: (i) we propose a simple but highly effective
self-training framework that adapts human 3D mesh recovery systems to consumer
videos and demonstrate its application to two recent systems; (ii) we introduce
evaluation protocols and keypoint annotations for 13K frames across four
consumer video datasets for studying this task, including evaluations on
out-of-image keypoints; and (iii) we show that our method substantially
improves PCK and human-subject judgments compared to baselines, both on test
videos from the dataset it was trained on, as well as on three other datasets
without further adaptation. Project website:
this https URL

本文提出了一种简单而高效的自学习框架，以适应消费视频数据的人体三维网格恢复系统，并在此基础上进行 13K 帧的评估以及对任务的关键点注释，结果显示我们的方法在测试视频上具有较高的准确性和人为判断水平。

从部分观察中获取全身意识

Full-Body Awareness from Partial Observations

We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color
image of a clothed person. Our method is based on a deep implicit
function-based representation to learn latent voxel features using a
structure-aware 3D U-Net, to constrain the model in two ways: first, to resolve
feature ambiguities in query point encoding, second, to serve as a coarse human
shape proxy to regularize the high-resolution mesh and encourage global shape
regularity. We show that, by both encoding query points and constraining global
shape using latent voxel features, the reconstruction we obtain for clothed
human meshes exhibits less shape distortion and improved surface details
compared to competing methods. We evaluate Geo-PIFu on a recent human mesh
public dataset that is $10 \times$ larger than the private commercial dataset
used in PIFu and previous derivative work. On average, we exceed the state of
the art by $42.7\%$ reduction in Chamfer and Point-to-Surface Distances, and
$19.4\%$ reduction in normal estimation errors.

Geo-PIFu 是一种基于深度多层次 U-Net 结构的隐式函数的方法，通过学习隐含体素特征来从穿着衣物的人的单眼彩色图像中恢复三维网格，并通过约束模型在两个方面以提高重建效果：第一，在查询点编码中解决特征歧义；第二，在粗糙的人体形状代理中规范化高分辨率网格并鼓励全局形状规则性，实验结果表明，与竞争方法相比，Geo-PIFu 可以得到更少的形状畸变和更好的表面细节。