Reconstructing 3D human shapes from 2D images has received increasing
attention recently due to its fundamental support for many high-level 3D
applications. Compared with natural images, freehand sketches are much more
flexible to depict various shapes, providing a high potential and valuable way
for 3D human reconstruction. However, such a task is highly challenging. The
sparse abstract characteristics of sketches add severe difficulties, such as
arbitrariness, inaccuracy, and lacking image details, to the already badly
ill-posed problem of 2D-to-3D reconstruction. Although current methods have
achieved great success in reconstructing 3D human bodies from a single-view
image, they do not work well on freehand sketches. In this paper, we propose a
novel sketch-driven multi-faceted decoder network termed SketchBodyNet to
address this task. Specifically, the network consists of a backbone and three
separate attention decoder branches, where a multi-head self-attention module
is exploited in each decoder to obtain enhanced features, followed by a
multi-layer perceptron. The multi-faceted decoders aim to predict the camera,
shape, and pose parameters, respectively, which are then associated with the
SMPL model to reconstruct the corresponding 3D human mesh. In learning,
existing 3D meshes are projected via the camera parameters into 2D synthetic
sketches with joints, which are combined with the freehand sketches to optimize
the model. To verify our method, we collect a large-scale dataset of about 26k
freehand sketches and their corresponding 3D meshes containing various poses of
human bodies from 14 different angles. Extensive experimental results
demonstrate our SketchBodyNet achieves superior performance in reconstructing
3D human meshes from freehand sketches.

本研究提出了一种名为 SketchBodyNet 的基于手绘草图的多方面解码网络，用于从手绘草图中重建 3D 人体网格模型，实验证明该方法在从手绘草图中重建 3D 人体网格方面取得了优异的性能。

SketchBodyNet：基于草图驱动的多方面解码网络用于 3D 人体重建

SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D  Human Reconstruction

We introduce a new method for non-rigid registration of 3D human shapes. Our
proposed pipeline builds upon a given parametric model of the human, and makes
use of the functional map representation for encoding and inferring shape maps
throughout the registration process. This combination endows our method with
robustness to a large variety of nuisances observed in practical settings,
including non-isometric transformations, downsampling, topological noise, and
occlusions; further, the pipeline can be applied invariably across different
shape representations (e.g. meshes and point clouds), and in the presence of
(even dramatic) missing parts such as those arising in real-world depth sensing
applications. We showcase our method on a selection of challenging tasks,
demonstrating results in line with, or even surpassing, state-of-the-art
methods in the respective areas.

本文介绍了一种针对三维人体形状的非刚性配准方法，该方法基于人体的参数化模型，并利用功能映射表示来在整个配准过程中编码和推断形状映射。该方法具有鲁棒性，可以应对实际环境中出现的各种干扰问题，包括非等距变换、降采样、拓扑噪声和遮挡等，在不同的形状表示下都可以适用，并且即使在出现缺失部分的情况下，也可以应用于实际的深度感测应用中。我们展示了该方法在一系列具有挑战性的任务上的效果，并且证明了该方法在相应领域达到了甚至超越了同类方法的效果。