Traditional methods for constructing high-quality, personalized head avatars
from monocular videos demand extensive face captures and training time, posing
a significant challenge for scalability. This paper introduces a novel approach
to create high quality head avatar utilizing only a single or a few images per
user. We learn a generative model for 3D animatable photo-realistic head avatar
from a multi-view dataset of expressions from 2407 subjects, and leverage it as
a prior for creating personalized avatar from few-shot images. Different from
previous 3D-aware face generative models, our prior is built with a
3DMM-anchored neural radiance field backbone, which we show to be more
effective for avatar creation through auto-decoding based on few-shot inputs.
We also handle unstable 3DMM fitting by jointly optimizing the 3DMM fitting and
camera calibration that leads to better few-shot adaptation. Our method
demonstrates compelling results and outperforms existing state-of-the-art
methods for few-shot avatar adaptation, paving the way for more efficient and
personalized avatar creation.

使用少量图像创建高质量的个性化头像，通过学习生成模型和 3DMM 锚定的神经辐射场骨干构建先验，实现基于少量输入图像的自动解码，优化 3DMM 拟合和相机校准以改进少样本自适应，开创了创建更高效个性化头像的道路，超越现有最先进的方法。

One2Avatar: 少样本用户自适应的生成式隐式头像

One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation

Reconstructing a dynamic human with loose clothing is an important but
difficult task. To address this challenge, we propose a method named DLCA-Recon
to create human avatars from monocular videos. The distance from loose clothing
to the underlying body rapidly changes in every frame when the human freely
moves and acts. Previous methods lack effective geometric initialization and
constraints for guiding the optimization of deformation to explain this
dramatic change, resulting in the discontinuous and incomplete reconstruction
surface. To model the deformation more accurately, we propose to initialize an
estimated 3D clothed human in the canonical space, as it is easier for
deformation fields to learn from the clothed human than from SMPL. With both
representations of explicit mesh and implicit SDF, we utilize the physical
connection information between consecutive frames and propose a dynamic
deformation field (DDF) to optimize deformation fields. DDF accounts for
contributive forces on loose clothing to enhance the interpretability of
deformations and effectively capture the free movement of loose clothing.
Moreover, we propagate SMPL skinning weights to each individual and refine pose
and skinning weights during the optimization to improve skinning
transformation. Based on more reasonable initialization and DDF, we can
simulate real-world physics more accurately. Extensive experiments on public
and our own datasets validate that our method can produce superior results for
humans with loose clothing compared to the SOTA methods.

使用 DLCA-Recon 方法，从单目视频中创建人类化身，模拟松散服装的运动并优化变形场，以更准确地重建动态人物表面。

DLCA-Recon: 动态宽松服装虚拟化重建

DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular  Videos

We study the problem of mapping an input image to a tied pair consisting of a
vector of parameters and an image that is created using a graphical engine from
the vector of parameters. The mapping's objective is to have the output image
as similar as possible to the input image. During training, no supervision is
given in the form of matching inputs and outputs.
This learning problem extends two literature problems: unsupervised domain
adaptation and cross domain transfer. We define a generalization bound that is
based on discrepancy, and employ a GAN to implement a network solution that
corresponds to this bound. Experimentally, our method is shown to solve the
problem of automatically creating avatars.

本文研究将输入图像映射到由参数向量和图像组成的绑定对中的问题，并提出了一种基于差异的泛化界限和采用 GAN 实现网络解决方案的方法。实验表明，该方法可以解决自动生成头像的问题。