Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism. In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene. While our palette-based bases are view-independent, we also predict a view-dependent function to capture the color residual (e.g., specular shading). During training, we jointly optimize the basis functions and the color palettes, and we also introduce novel regularizers to encourage the spatial coherence of the decomposition. Our method allows users to efficiently edit the appearance of the 3D scene by modifying the color palettes. We also extend our framework with compressed semantic features for semantic-aware appearance editing. We demonstrate that our technique is superior to baseline methods both quantitatively and qualitatively for appearance editing of complex real-world scenes.

The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment. Current methods either build on explicit 3D morphable meshes (3DMM) or exploit neural implicit representations. The former are limited by fixed topology, while the latter are non-trivial to deform and inefficient to render. Furthermore, existing approaches entangle lighting in the color estimation, thus they are limited in re-rendering the avatar in new environments. In contrast, we propose PointAvatar, a deformable point-based representation that disentangles the source color into intrinsic albedo and normal-dependent shading. We demonstrate that PointAvatar bridges the gap between existing mesh- and implicit representations, combining high-quality geometry and appearance with topological flexibility, ease of deformation and rendering efficiency. We show that our method is able to generate animatable 3D avatars using monocular videos from multiple sources including hand-held smartphones, laptop webcams and internet videos, achieving state-of-the-art quality in challenging cases where previous methods fail, e.g., thin hair strands, while being significantly more efficient in training than competing methods.

This work introduces alternating latent topologies (ALTO) for high-fidelity reconstruction of implicit 3D surfaces from noisy point clouds. Previous work identifies that the spatial arrangement of latent encodings is important to recover detail. One school of thought is to encode a latent vector for each point (point latents). Another school of thought is to project point latents into a grid (grid latents) which could be a voxel grid or triplane grid. Each school of thought has tradeoffs. Grid latents are coarse and lose high-frequency detail. In contrast, point latents preserve detail. However, point latents are more difficult to decode into a surface, and quality and runtime suffer. In this paper, we propose ALTO to sequentially alternate between geometric representations, before converging to an easy-to-decode latent. We find that this preserves spatial expressiveness and makes decoding lightweight. We validate ALTO on implicit 3D recovery and observe not only a performance improvement over the state-of-the-art, but a runtime improvement of 3-10$\times$. Project website at https://visual.ee.ucla.edu/alto.htm/.

Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data. We introduce SinGRAF, a 3D-aware generative model that is trained with a few input images of a single scene. Once trained, SinGRAF generates different realizations of this 3D scene that preserve the appearance of the input while varying scene layout. For this purpose, we build on recent progress in 3D GAN architectures and introduce a novel progressive-scale patch discrimination approach during training. With several experiments, we demonstrate that the results produced by SinGRAF outperform the closest related works in both quality and diversity by a large margin.

Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring them into a set of axis-aligned triplane feature representations. Thus, our 3D training scenes are all represented by 2D feature planes, and we can directly train existing 2D diffusion models on these representations to generate 3D neural fields with high quality and diversity, outperforming alternative approaches to 3D-aware generation. Our approach requires essential modifications to existing triplane factorization pipelines to make the resulting features easy to learn for the diffusion model. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.

神经表示是表示形状的流行，因为它们可以学习形式传感器数据，并用于数据清理，模型完成，形状编辑和形状合成。当前的神经表示形式可以归类为对单个对象实例的过度拟合或表示对象集合。但是，都不允许对神经场景表示的准确编辑：一方面，过度拟合对象实现高度准确的重建的方法，但不能推广到看不见的对象配置，因此无法支持编辑；另一方面，代表具有变化的对象家族的方法确实概括了，但仅产生近似重建。我们建议Neuform使用最适合每个形状区域的一个：可靠数据的过拟合表示，以及可靠的可用数据以及其他任何地方的可推广表示形式，以适应过度拟合和可推广表示的优势。我们通过精心设计的体系结构和一种将两个表示网络权重融合在一起的方法，避免接缝和其他工件。我们展示了成功重新配置人类设计的形状的部分，例如椅子，表和灯，同时保留语义完整性和过度拟合形状表示的准确性。我们与两个最先进的竞争对手进行了比较，并在合理性和结果编辑的忠诚度方面取得了明显的改善。

生成模型已成为许多图像合成和编辑任务的基本构件。该领域的最新进展还使得能够生成具有多视图或时间一致性的高质量3D或视频内容。在我们的工作中，我们探索了学习无条件生成3D感知视频的4D生成对抗网络（GAN）。通过将神经隐式表示与时间感知歧视器相结合，我们开发了一个GAN框架，该框架仅通过单眼视频进行监督的3D视频。我们表明，我们的方法学习了可分解的3D结构和动作的丰富嵌入，这些结构和动作可以使时空渲染的新视觉效果，同时以与现有3D或视频gan相当的质量产生图像。

仅使用单视2D照片的收藏集对3D感知生成对抗网络（GAN）的无监督学习最近取得了很多进展。然而，这些3D gan尚未证明人体，并且现有框架的产生的辐射场不是直接编辑的，从而限制了它们在下游任务中的适用性。我们通过开发一个3D GAN框架来解决这些挑战的解决方案，该框架学会在规范的姿势中生成人体或面部的辐射场，并使用显式变形场将其扭曲成所需的身体姿势或面部表达。使用我们的框架，我们展示了人体的第一个高质量的辐射现场生成结果。此外，我们表明，与未接受明确变形训练的3D GAN相比，在编辑其姿势或面部表情时，我们的变形感知训练程序可显着提高产生的身体或面部的质量。

冷冻电子显微镜（Cryo-EM）已成为结构生物学中基本重要性的工具，帮助我们了解生活的基本构建基础。冷冻EM的算法挑战是共同估计未知的3D姿势和来自数百万个极其嘈杂的2D图像的生物分子的3D电子散射潜力。但是，由于其高度计算和内存成本，现有的重建算法无法轻易地与迅速增长的低温EM数据集尺寸保持同步。我们介绍了Cryoai，这是一种用于均匀构象的从头算重建算法，该构型使用基于直接梯度的粒子姿势优化和来自单粒子冷冻EM数据的电子散射电位。冷冻ai结合了一个学识渊博的编码器，该编码器将每个粒子图像的姿势与基于物理的解码器进行汇总，以将每个粒子图像汇总到散射势体积的隐式表示中。该卷存储在傅立叶域中以提高计算效率，并利用现代坐标网络体系结构来提高内存效率。结合对称损耗函数，该框架可在模拟和实验数据中与最先进的冷冻EM求解器达到质量的结果，对于大型数据集而言，一个数量级的阶数级，并且具有明显低的存储器需求现有方法。

使用单视图2D照片仅集合，无监督的高质量多视图 - 一致的图像和3D形状一直是一个长期存在的挑战。现有的3D GAN是计算密集型的，也是没有3D-一致的近似;前者限制了所生成的图像的质量和分辨率，并且后者对多视图一致性和形状质量产生不利影响。在这项工作中，我们提高了3D GAN的计算效率和图像质量，而无需依赖这些近似。为此目的，我们介绍了一种表现力的混合明确隐式网络架构，与其他设计选择一起，不仅可以实时合成高分辨率多视图一致图像，而且还产生高质量的3D几何形状。通过解耦特征生成和神经渲染，我们的框架能够利用最先进的2D CNN生成器，例如Stylega2，并继承它们的效率和表现力。在其他实验中，我们展示了与FFHQ和AFHQ猫的最先进的3D感知合成。