We present a novel approach to the generation of static and articulated 3D
assets that has a 3D autodecoder at its core. The 3D autodecoder framework
embeds properties learned from the target dataset in the latent space, which
can then be decoded into a volumetric representation for rendering
view-consistent appearance and geometry. We then identify the appropriate
intermediate volumetric latent space, and introduce robust normalization and
de-normalization operations to learn a 3D diffusion from 2D images or monocular
videos of rigid or articulated objects. Our approach is flexible enough to use
either existing camera supervision or no camera information at all -- instead
efficiently learning it during training. Our evaluations demonstrate that our
generation results outperform state-of-the-art alternatives on various
benchmark datasets and metrics, including multi-view image datasets of
synthetic objects, real in-the-wild videos of moving people, and a large-scale,
real video dataset of static objects.

本文提出了一种新颖的生成静态和关节 3D 资产的方法，其中核心是一个 3D 自解码器框架，将目标数据集中学习到的属性嵌入潜在空间，然后通过解码来渲染具有一致性的外观和几何体积表示；在不需要摄像头信息的情况下，通过学习来进行更加高效的训练，实现了在各种基准数据集和指标上超越同类别替代方法的影响力。

自动解码潜在的三维扩散模型

AutoDecoding Latent 3D Diffusion Models

This paper presents a method to reconstruct high-quality textured 3D models
from single images. Current methods rely on datasets with expensive
annotations; multi-view images and their camera parameters. Our method relies
on GAN generated multi-view image datasets which have a negligible annotation
cost. However, they are not strictly multi-view consistent and sometimes GANs
output distorted images. This results in degraded reconstruction qualities. In
this work, to overcome these limitations of generated datasets, we have two
main contributions which lead us to achieve state-of-the-art results on
challenging objects: 1) A robust multi-stage learning scheme that gradually
relies more on the models own predictions when calculating losses, 2) A novel
adversarial learning pipeline with online pseudo-ground truth generations to
achieve fine details. Our work provides a bridge from 2D supervisions of GAN
models to 3D reconstruction models and removes the expensive annotation
efforts. We show significant improvements over previous methods whether they
were trained on GAN generated multi-view images or on real images with
expensive annotations. Please visit our web-page for 3D visuals:
this https URL

本文介绍了一种使用生成式对抗网络（GAN）生成的多视角图像数据集重建高质量纹理三维模型的方法，其具有可忽略的注释成本，并通过多阶段学习和在线伪标签生成的新颖对抗学习流水线实现了精细的细节并显著改善了之前的方法。

从 2D GAN 数据中渐进式学习 3D 重建网络

Progressive Learning of 3D Reconstruction Network from 2D GAN Data

We present a general paradigm for dynamic 3D reconstruction from multiple
independent and uncontrolled image sources having arbitrary temporal sampling
density and distribution. Our graph-theoretic formulation models the
Spatio-temporal relationships among our observations in terms of the joint
estimation of their 3D geometry and its discrete Laplace operator. Towards this
end, we define a tri-convex optimization framework that leverages the geometric
properties and dependencies found among a Euclideanshape-space and the discrete
Laplace operator describing its local and global topology. We present a
reconstructability analysis, experiments on motion capture data and multi-view
image datasets, as well as explore applications to geometry-based event
segmentation and data association.

本论文提出了一种通用的范式来处理来自多个独立和不受控制的图像来源的 3D 动态重建问题，该问题具有任意时间采样密度和分布，通过图表论的方法建立空间 - 时间关系，通过三重凸优化框架来进行重建，在运动捕捉数据和多视图图像数据集上进行实验，并探讨几何事件分割和数据关联的应用。