Remarkable progress has been made in 3D reconstruction of rigid structures
from a video or a collection of images. However, it is still challenging to
reconstruct nonrigid structures from RGB inputs, due to its under-constrained
nature. While template-based approaches, such as parametric shape models, have
achieved great success in modeling the "closed world" of known object
categories, they cannot well handle the "open-world" of novel object categories
or outlier shapes. In this work, we introduce a template-free approach to learn
3D shapes from a single video. It adopts an analysis-by-synthesis strategy that
forward-renders object silhouette, optical flow, and pixel values to compare
with video observations, which generates gradients to adjust the camera, shape
and motion parameters. Without using a category-specific shape template, our
method faithfully reconstructs nonrigid 3D structures from videos of human,
animals, and objects of unknown classes. Code will be available at
lasr-google.github.io .

本文提出了一种无模板的分析合成策略的方法，可以从单个视频中学习三维形状，有效地重建了人、动物和未知类别对象的非刚性三维结构。

LASR: 从单目视频学习关节形状重建

LASR: Learning Articulated Shape Reconstruction from a Monocular Video

A fundamental problem in computational chemistry is to find a set of
reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction.
Existing state-of-the-art methods rely on matching the target molecule with a
large set of reaction templates, which are very computationally expensive and
also suffer from the problem of coverage. In this paper, we propose a novel
template-free approach called G2Gs by transforming a target molecular graph
into a set of reactant molecular graphs. G2Gs first splits the target molecular
graph into a set of synthons by identifying the reaction centers, and then
translates the synthons to the final reactant graphs via a variational graph
translation framework. Experimental results show that G2Gs significantly
outperforms existing template-free approaches by up to 63% in terms of the
top-1 accuracy and achieves a performance close to that of state-of-the-art
template based approaches, but does not require domain knowledge and is much
more scalable.

本文提出了一种基于分子图转换的无模板逆合成预测方法 G2Gs，可以将一个目标分子图转化为一组反应物分子图，通过识别反应中心将目标分子图分解为一组合成子，再通过图形翻译技术将它们转化为反应物图。实验结果表明，G2Gs 的表现接近于最先进的基于模板的方法，且不需要领域知识且更加可扩展。