Neural radiance fields (NeRFs) have achieved impressive view synthesis
results by learning an implicit volumetric representation from multi-view
images. To project the implicit representation into an image, NeRF employs
volume rendering that approximates the continuous integrals of rays as an
accumulation of the colors and densities of the sampled points. Although this
approximation enables efficient rendering, it ignores the direction information
in point intervals, resulting in ambiguous features and limited reconstruction
quality. In this paper, we propose an anisotropic neural representation
learning method that utilizes learnable view-dependent features to improve
scene representation and reconstruction. We model the volumetric function as
spherical harmonic (SH)-guided anisotropic features, parameterized by
multilayer perceptrons, facilitating ambiguity elimination while preserving the
rendering efficiency. To achieve robust scene reconstruction without anisotropy
overfitting, we regularize the energy of the anisotropic features during
training. Our method is flexiable and can be plugged into NeRF-based
frameworks. Extensive experiments show that the proposed representation can
boost the rendering quality of various NeRFs and achieve state-of-the-art
rendering performance on both synthetic and real-world scenes.

通过学习隐式体积表示来改善场景表示和重建的方法，利用学习的视角相关特征将体积函数建模为球谐（SH）引导的各向异性特征，通过参数化多层感知机提高渲染质量，并通过能量正则化实现鲁棒的场景重建，该方法可在基于 NeRF 的框架中应用。广泛的实验证明了所提出表示方法对于多种 NeRF 和合成真实场景的渲染质量的提升，达到了最先进的渲染性能。

高质量神经渲染的各向异性神经表征学习

Anisotropic Neural Representation Learning for High-Quality Neural  Rendering

Human motion retargeting aims to transfer the motion of one person in a
"driving" video or set of images to another person. Existing efforts leverage a
long training video from each target person to train a subject-specific motion
transfer model. However, the scalability of such methods is limited, as each
model can only generate videos for the given target subject, and such training
videos are labor-intensive to acquire and process. Few-shot motion transfer
techniques, which only require one or a few images from a target, have recently
drawn considerable attention. Methods addressing this task generally use either
2D or explicit 3D representations to transfer motion, and in doing so,
sacrifice either accurate geometric modeling or the flexibility of an
end-to-end learned representation. Inspired by the Transformable Bottleneck
Network, which renders novel views and manipulations of rigid objects, we
propose an approach based on an implicit volumetric representation of the image
content, which can then be spatially manipulated using volumetric flow fields.
We address the challenging question of how to aggregate information across
different body poses, learning flow fields that allow for combining content
from the appropriate regions of input images of highly non-rigid human subjects
performing complex motions into a single implicit volumetric representation.
This allows us to learn our 3D representation solely from videos of moving
people. Armed with both 3D object understanding and end-to-end learned
rendering, this categorically novel representation delivers state-of-the-art
image generation quality, as shown by our quantitative and qualitative
evaluations.

该研究提出了一种基于隐式体积表示的方法来解决人的运动重定位问题，该方法可以从移动人的视频中仅学习到 3D 表示，具有很高的图像生成质量。