Much progress has been made in reconstructing garments from an image or a
video. However, none of existing works meet the expectations of digitizing
high-quality animatable dynamic garments that can be adjusted to various unseen
poses. In this paper, we propose the first method to recover high-quality
animatable dynamic garments from monocular videos without depending on scanned
data. To generate reasonable deformations for various unseen poses, we propose
a learnable garment deformation network that formulates the garment
reconstruction task as a pose-driven deformation problem. To alleviate the
ambiguity estimating 3D garments from monocular videos, we design a
multi-hypothesis deformation module that learns spatial representations of
multiple plausible deformations. Experimental results on several public
datasets demonstrate that our method can reconstruct high-quality dynamic
garments with coherent surface details, which can be easily animated under
unseen poses. The code will be provided for research purposes.

首次提出从单目视频中恢复高质量可动态调整的服装的方法，通过可学习的服装变形网络和多假设变形模块实现，实验结果表明可生成具有连贯表面细节的高质量动态服装，并可在未见过的姿势下轻松实现动画效果。

从单目视频中高质量可动画的动态服装重构

High-Quality Animatable Dynamic Garment Reconstruction from Monocular  Videos

Animating virtual avatars with free-view control is crucial for various
applications like virtual reality and digital entertainment. Previous studies
have attempted to utilize the representation power of the neural radiance field
(NeRF) to reconstruct the human body from monocular videos. Recent works
propose to graft a deformation network into the NeRF to further model the
dynamics of the human neural field for animating vivid human motions. However,
such pipelines either rely on pose-dependent representations or fall short of
motion coherency due to frame-independent optimization, making it difficult to
generalize to unseen pose sequences realistically. In this paper, we propose a
novel framework MonoHuman, which robustly renders view-consistent and
high-fidelity avatars under arbitrary novel poses. Our key insight is to model
the deformation field with bi-directional constraints and explicitly leverage
the off-the-peg keyframe information to reason the feature correlations for
coherent results. Specifically, we first propose a Shared Bidirectional
Deformation module, which creates a pose-independent generalizable deformation
field by disentangling backward and forward deformation correspondences into
shared skeletal motion weight and separate non-rigid motions. Then, we devise a
Forward Correspondence Search module, which queries the correspondence feature
of keyframes to guide the rendering network. The rendered results are thus
multi-view consistent with high fidelity, even under challenging novel pose
settings. Extensive experiments demonstrate the superiority of our proposed
MonoHuman over state-of-the-art methods.

本论文提出了一种名为 MonoHuman 的新型框架，该框架使用共享双向变形模块和前向对应搜索模块来创造出一个可塑性质高的形变场，其利用离线关键帧信息来推断人体不同部位运动的相关特征，使得生成的虚拟角色在姿势、细节和视角等方面更为真实。与其他现有方法相比，该框架产生的效果更为卓越。

MonoHuman：从单目视频中生成可动的人类神经网络场

MonoHuman: Animatable Human Neural Field from Monocular Video

We present the first one-shot personalized sketch segmentation method. We aim
to segment all sketches belonging to the same category provisioned with a
single sketch with a given part annotation while (i) preserving the parts
semantics embedded in the exemplar, and (ii) being robust to input style and
abstraction. We refer to this scenario as personalized. With that, we
importantly enable a much-desired personalization capability for downstream
fine-grained sketch analysis tasks. To train a robust segmentation module, we
deform the exemplar sketch to each of the available sketches of the same
category. Our method generalizes to sketches not observed during training. Our
central contribution is a sketch-specific hierarchical deformation network.
Given a multi-level sketch-strokes encoding obtained via a graph convolutional
network, our method estimates rigid-body transformation from the target to the
exemplar, on the upper level. Finer deformation from the exemplar to the
globally warped target sketch is further obtained through stroke-wise
deformations, on the lower level. Both levels of deformation are guided by mean
squared distances between the keypoints learned without supervision, ensuring
that the stroke semantics are preserved. We evaluate our method against the
state-of-the-art segmentation and perceptual grouping baselines re-purposed for
the one-shot setting and against two few-shot 3D shape segmentation methods. We
show that our method outperforms all the alternatives by more than $10\%$ on
average. Ablation studies further demonstrate that our method is robust to
personalization: changes in input part semantics and style differences.

本文提出了一种针对手绘图像的分割算法，该算法通过使用个性化的提取部件注释，以及简单的 sketch 进行训练，实现了对手绘图像的分割。该算法通过使用简单的 sketch 训练的多阶段变形网络进行随意手绘的分割，相比基于 few-shot 和 state-of-the-art 方法提高了 10% 准确率