AttriHuman-3D：基于属性分解与索引的可编辑3D人体化身生成
AttriHuman-3D: Editable 3D Human Avatar Generation with Attribute Decomposition and Indexing
AI总结：
AttriHuman-3D通过属性分解与索引机制，在六特征平面属性空间中实现可编辑3D人体化身生成，结合属性索引、正交投影正则化及超隐变量训练策略，实现高质量细粒度局部编辑。
摘要：
可编辑的3D感知生成技术支持用户交互式编辑，近年来发展迅速。然而，现有的可编辑3D GAN要么无法实现高精度的局部编辑，要么面临巨大的计算开销。我们提出AttriHuman-3D，一种可编辑的3D人体生成模型，通过属性分解与索引来解决上述问题。该模型的核心思想是在一个具有六个特征平面的整体属性空间中生成所有属性（如人体、头发、衣服等），然后利用不同的属性索引对这些属性进行分解和操控。为了从生成的特征平面中精确提取不同属性的特征，我们提出了一种新颖的属性索引方法以及正交投影正则化来增强解耦能力。我们还引入了超隐变量训练策略和属性特定采样策略，以避免风格纠缠和判别器的误导性惩罚。我们的方法允许用户在生成的3D人体化身中交互式地编辑选定属性，同时保持其他属性不变。定性和定量实验均表明，我们的模型在不同属性之间提供了强大的解耦能力，支持细粒度图像编辑，并能生成高质量的3D人体化身。
英文摘要：
Editable 3D-aware generation, which supports user-interacted editing, has witnessed rapid development recently. However, existing editable 3D GANs either fail to achieve high-accuracy local editing or suffer from huge computational costs. We propose AttriHuman-3D, an editable 3D human generation model, which address the aforementioned problems with attribute decomposition and indexing. The core idea of the proposed model is to generate all attributes (e.g. human body, hair, clothes and so on) in an overall attribute space with six feature planes, which are then decomposed and manipulated with different attribute indexes. To precisely extract features of different attributes from the generated feature planes, we propose a novel attribute indexing method as well as an orthogonal projection regularization to enhance the disentanglement. We also introduce a hyper-latent training strategy and an attribute-specific sampling strategy to avoid style entanglement and misleading punishment from the discriminator. Our method allows users to interactively edit selected attributes in the generated 3D human avatars while keeping others fixed. Both qualitative and quantitative experiments demonstrate that our model provides a strong disentanglement between different attributes, allows fine-grained image editing and generates high-quality 3D human avatars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
