Recent neural human representations can produce high-quality multi-view
rendering but require using dense multi-view inputs and costly training. They
are hence largely limited to static models as training each frame is
infeasible. We present HumanNeRF - a generalizable neural representation - for
high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet
assists NeRF by avoiding per-scene training, HumanNeRF employs an aggregated
pixel-alignment feature across multi-view inputs along with a pose embedded
non-rigid deformation field for tackling dynamic motions. The raw HumanNeRF can
already produce reasonable rendering on sparse video inputs of unseen subjects
and camera settings. To further improve the rendering quality, we augment our
solution with an appearance blending module for combining the benefits of both
neural volumetric rendering and neural texture blending. Extensive experiments
on various multi-view dynamic human datasets demonstrate the generalizability
and effectiveness of our approach in synthesizing photo-realistic free-view
humans under challenging motions and with very sparse camera view inputs.

HumanNeRF 是一种通用的神经表示方法，它沿着多视角输入引入了一个聚合像素对齐特征，并使用一种姿态嵌入的非刚性变形场来处理动态运动，进而通过外观融合模块进一步提高渲染质量，实现了在各种多视角动态人体数据集上合成具有照片般逼真的自由视角人类的高效和通用性。

HumanNeRF：从稀疏输入高效生成人体辐射场

HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs

We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D
spatial-temporal representation of a dynamic scene from a set of RGB images.
Key to our approach is the use of a neural implicit representation that learns
to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing
consistency across different modalities, our representation enables multi-view
rendering in diverse dynamic scenes, including water pouring, robotic
interaction, and real images, outperforming state-of-the-art methods for
spatial-temporal view synthesis. Our approach works even when inputs images are
captured with only one camera. We further demonstrate that the learned
representation can serve as an implicit scene prior, enabling video processing
tasks such as image super-resolution and de-noising without any additional
supervision.

我们提出了一种称为神经辐射流（NeRFlow）的方法，可以从一组 RGB 图像中学习动态场景的 4D 时空表示，使得不需要多个相机拍摄即可实现多视角渲染，同时可用作视频处理任务的隐式场景先验。

用于四维视图合成和视频处理的神经辐射流

Neural Radiance Flow for 4D View Synthesis and Video Processing

In this work, we propose an end-to-end framework to learn local multi-view
descriptors for 3D point clouds. To adopt a similar multi-view representation,
existing studies use hand-crafted viewpoints for rendering in a preprocessing
stage, which is detached from the subsequent descriptor learning stage. In our
framework, we integrate the multi-view rendering into neural networks by using
a differentiable renderer, which allows the viewpoints to be optimizable
parameters for capturing more informative local context of interest points. To
obtain discriminative descriptors, we also design a soft-view pooling module to
attentively fuse convolutional features across views. Extensive experiments on
existing 3D registration benchmarks show that our method outperforms existing
local descriptors both quantitatively and qualitatively.

提出了一个端到端的框架，用于学习 3D 点云的本地多视图描述符。利用可微分的渲染器将多视图表示集成到神经网络中，设计了软视图池化模块来聚合视角间的卷积特征，实验结果表明，本文方法在现有的 3D 配准基准上更优秀。