Scanning real-life scenes with modern registration devices typically gives
incomplete point cloud representations, primarily due to the limitations of
partial scanning, 3D occlusions, and dynamic light conditions. Recent works on
processing incomplete point clouds have always focused on point cloud
completion. However, these approaches do not ensure consistency between the
completed point cloud and the captured images regarding color and geometry. We
propose using Generative Point-based NeRF (GPN) to reconstruct and repair a
partial cloud by fully utilizing the scanning images and the corresponding
reconstructed cloud. The repaired point cloud can achieve multi-view
consistency with the captured images at high spatial resolution. For the
finetunes of a single scene, we optimize the global latent condition by
incorporating an Auto-Decoder architecture while retaining multi-view
consistency. As a result, the generated point clouds are smooth, plausible, and
geometrically consistent with the partial scanning images. Extensive
experiments on ShapeNet demonstrate that our works achieve competitive
performances to the other state-of-the-art point cloud-based neural scene
rendering and editing performances.

使用生成式基于点云的逆向正向（Generative Point-based NeRF）方法通过利用扫描图像和对应的重建点云修复局部点云，从而实现高空间分辨率下与捕获的图像的多视角一致性。实验证明，该方法在点云基于神经场景渲染和编辑领域表现出与其他最先进方法竞争力的性能。

GPN: 生成点云模型的 NeRF

GPN: Generative Point-based NeRF

It is extremely challenging to create an animatable clothed human avatar from
RGB videos, especially for loose clothes due to the difficulties in motion
modeling. To address this problem, we introduce a novel representation on the
basis of recent neural scene rendering techniques. The core of our
representation is a set of structured local radiance fields, which are anchored
to the pre-defined nodes sampled on a statistical human body template. These
local radiance fields not only leverage the flexibility of implicit
representation in shape and appearance modeling, but also factorize cloth
deformations into skeleton motions, node residual translations and the dynamic
detail variations inside each individual radiance field. To learn our
representation from RGB data and facilitate pose generalization, we propose to
learn the node translations and the detail variations in a conditional
generative latent space. Overall, our method enables automatic construction of
animatable human avatars for various types of clothes without the need for
scanning subject-specific templates, and can generate realistic images with
dynamic details for novel poses. Experiment show that our method outperforms
state-of-the-art methods both qualitatively and quantitatively.

使用基于神经场景渲染技术的新型表示方法，将结构化本地辐射场锚定到人体模板节点上，将服装变形分解为骨架运动、节点残差翻译和每个辐射场内的动态细节变化，支持各种服装类型的自动构建可动画人形化身，并在新姿势下生成真实的动态细节图像。