Synthesizing 3D human avatars interacting realistically with a scene is an
important problem with applications in AR/VR, video games and robotics. Towards
this goal, we address the task of generating a virtual human -- hands and full
body -- grasping everyday objects. Existing methods approach this problem by
collecting a 3D dataset of humans interacting with objects and training on this
data. However, 1) these methods do not generalize to different object positions
and orientations, or to the presence of furniture in the scene, and 2) the
diversity of their generated full-body poses is very limited. In this work, we
address all the above challenges to generate realistic, diverse full-body
grasps in everyday scenes without requiring any 3D full-body grasping data. Our
key insight is to leverage the existence of both full-body pose and hand
grasping priors, composing them using 3D geometrical constraints to obtain
full-body grasps. We empirically validate that these constraints can generate a
variety of feasible human grasps that are superior to baselines both
quantitatively and qualitatively. See our webpage for more details:
this https URL

通过利用全身姿势和手部抓握先验，利用 3D 几何约束合成具有现实感和多样性的全身抓握，从而在现实场景中生成虚拟人类。

FLEX：无需全身抓取的全身抓取

FLEX: Full-Body Grasping Without Full-Body Grasps

Previous monocular depth estimation methods take a single view and directly
regress the expected results. Though recent advances are made by applying
geometrically inspired loss functions during training, the inference procedure
does not explicitly impose any geometrical constraint. Therefore these models
purely rely on the quality of data and the effectiveness of learning to
generalize. This either leads to suboptimal results or the demand of huge
amount of expensive ground truth labelled data to generate reasonable results.
In this paper, we show for the first time that the monocular depth estimation
problem can be reformulated as two sub-problems, a view synthesis procedure
followed by stereo matching, with two intriguing properties, namely i)
geometrical constraints can be explicitly imposed during inference; ii) demand
on labelled depth data can be greatly alleviated. We show that the whole
pipeline can still be trained in an end-to-end fashion and this new formulation
plays a critical role in advancing the performance. The resulting model
outperforms all the previous monocular depth estimation methods as well as the
stereo block matching method in the challenging KITTI dataset by only using a
small number of real training data. The model also generalizes well to other
monocular depth estimation benchmarks. We also discuss the implications and the
advantages of solving monocular depth estimation using stereo methods.

该研究论文采用视图合成过程和立体匹配相结合的方法，将单目深度估计问题分解为两个子问题，并明确地在推断过程中施加几何约束条件。经验证明，这种方法的性能优于以往的方法，并且只需使用少量的真实训练数据，同时也具有良好的泛化能力。