The widespread adoption of implicit neural representations, especially Neural
Radiance Fields (NeRF), highlights a growing need for editing capabilities in
implicit 3D models, essential for tasks like scene post-processing and 3D
content creation. Despite previous efforts in NeRF editing, challenges remain
due to limitations in editing flexibility and quality. The key issue is
developing a neural representation that supports local edits for real-time
updates. Current NeRF editing methods, offering pixel-level adjustments or
detailed geometry and color modifications, are mostly limited to static scenes.
This paper introduces SealD-NeRF, an extension of Seal-3D for pixel-level
editing in dynamic settings, specifically targeting the D-NeRF network. It
allows for consistent edits across sequences by mapping editing actions to a
specific timeframe, freezing the deformation network responsible for dynamic
scene representation, and using a teacher-student approach to integrate
changes.

本文介绍了 SealD-NeRF，它是 Seal-3D 的一个扩展，用于在动态环境中进行像素级编辑，特别针对 D-NeRF 网络。它通过将编辑操作映射到特定的时间框架，冻结负责动态场景表示的变形网络，并使用师生方法来集成更改，实现序列中的一致编辑。

SealD-NeRF: 动态场景的神经辐射场交互式像素级编辑

SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural  Radiance Fields

Recent advances in neural radiance fields enable novel view synthesis of
photo-realistic images in dynamic settings, which can be applied to scenarios
with human animation. Commonly used implicit backbones to establish accurate
models, however, require many input views and additional annotations such as
human masks, UV maps and depth maps. In this work, we propose ParDy-Human
(Parameterized Dynamic Human Avatar), a fully explicit approach to construct a
digital avatar from as little as a single monocular sequence. ParDy-Human
introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D
Gaussians are deformed by a human pose model to animate the avatar. Our method
is composed of two parts: A first module that deforms canonical 3D Gaussians
according to SMPL vertices and a consecutive module that further takes their
designed joint encodings and predicts per Gaussian deformations to deal with
dynamics beyond SMPL vertex deformations. Images are then synthesized by a
rasterizer. ParDy-Human constitutes an explicit model for realistic dynamic
human avatars which requires significantly fewer training views and images. Our
avatars learning is free of additional annotations such as masks and can be
trained with variable backgrounds while inferring full-resolution images
efficiently even on consumer hardware. We provide experimental evidence to show
that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and
THUman4.0 datasets both quantitatively and visually.

通过引入参数驱动的动力学到三维高斯喷点，本研究提出了一种完全明确的方法来由单一单眼序列构建数字化角色，并使用栅格化器合成图像。该方法无需额外注释并且能在消费硬件上高效推断全分辨率图像，实验证明其在数据集上在定量和视觉上均优于现有的技术。

可变形的 3D 高斯喷洒技术用于可动式人型角色模型

Deformable 3D Gaussian Splatting for Animatable Human Avatars

Human culture relies on innovation: our ability to continuously explore how
existing elements can be combined to create new ones. Innovation is not
solitary, it relies on collective search and accumulation. Reinforcement
learning (RL) approaches commonly assume that fully-connected groups are best
suited for innovation. However, human laboratory and field studies have shown
that hierarchical innovation is more robustly achieved by dynamic social
network structures. In dynamic settings, humans oscillate between innovating
individually or in small clusters, and then sharing outcomes with others. To
our knowledge, the role of social network structure on innovation has not been
systematically studied in RL. Here, we use a multi-level problem setting
(WordCraft), with three different innovation tasks to test the hypothesis that
the social network structure affects the performance of distributed RL
algorithms. We systematically design networks of DQNs sharing experiences from
their replay buffers in varying structures (fully-connected, small world,
dynamic, ring) and introduce a set of behavioral and mnemonic metrics that
extend the classical reward-focused evaluation framework of RL. Comparing the
level of innovation achieved by different social network structures across
different tasks shows that, first, consistent with human findings, experience
sharing within a dynamic structure achieves the highest level of innovation in
tasks with a deceptive nature and large search spaces. Second, experience
sharing is not as helpful when there is a single clear path to innovation.
Third, the metrics we propose, can help understand the success of different
social network structures on different tasks, with the diversity of experiences
on an individual and group level lending crucial insights.

本文在多层问题设置 (WordCraft）中进行实验，测试社交网络结构是否会影响分布式 RL 算法的性能，并发现类人实验表明，动态社交网络内的经验共享达到了具有欺骗性的任务中最高的创新水平。