For reconstructing high-fidelity human 3D models from monocular videos, it is
crucial to maintain consistent large-scale body shapes along with finely
matched subtle wrinkles. This paper explores the observation that the per-frame
rendering results can be factorized into a pose-independent component and a
corresponding pose-dependent equivalent to facilitate frame consistency. Pose
adaptive textures can be further improved by restricting frequency bands of
these two components. In detail, pose-independent outputs are expected to be
low-frequency, while highfrequency information is linked to pose-dependent
factors. We achieve a coherent preservation of both coarse body contours across
the entire input video and finegrained texture features that are time variant
with a dual-branch network with distinct frequency components. The first branch
takes coordinates in canonical space as input, while the second branch
additionally considers features outputted by the first branch and pose
information of each frame. Our network integrates the information predicted by
both branches and utilizes volume rendering to generate photo-realistic 3D
human images. Through experiments, we demonstrate that our network surpasses
the neural radiance fields (NeRF) based state-of-the-art methods in preserving
high-frequency details and ensuring consistent body contours.

使用双分支网络和体积渲染生成照片级逼真的 3D 人体图像，通过限制频率分量和观察帧一致性，实现了保留高频细节和确保身体轮廓一致性的高保真度人体 3D 模型重建。

通过分解神经场表示可动化头像

Representing Animatable Avatar via Factorized Neural Fields

High-fidelity human 3D models can now be learned directly from videos,
typically by combining a template-based surface model with neural
representations. However, obtaining a template surface requires expensive
multi-view capture systems, laser scans, or strictly controlled conditions.
Previous methods avoid using a template but rely on a costly or ill-posed
mapping from observation to canonical space. We propose a hybrid point-based
representation for reconstructing animatable characters that does not require
an explicit surface model, while being generalizable to novel poses. For a
given video, our method automatically produces an explicit set of 3D points
representing approximate canonical geometry, and learns an articulated
deformation model that produces pose-dependent point transformations. The
points serve both as a scaffold for high-frequency neural features and an
anchor for efficiently mapping between observation and canonical space. We
demonstrate on established benchmarks that our representation overcomes
limitations of prior work operating in either canonical or in observation
space. Moreover, our automatic point extraction approach enables learning
models of human and animal characters alike, matching the performance of the
methods using rigged surface templates despite being more general. Project
website: this https URL

本文提出一种新的混合点表示方法，通过自动产生的几何数据点集作为支架和简便的映射方法，学习人形和动物角色的可动形变模型，相较于之前的方法，该方法无需昂贵的采集设备或受控环境的条件，且更具普适性和可推广性。