Skel3D：骨架引导的新视角合成
Skel3D: Skeleton Guided Novel View Synthesis
AI总结：
针对单目开放集新视角合成，本文提出Skel3D，通过在RCN层后加入骨架引导层，利用Objaverse骨骼结构增强扩散模型的姿态准确性与多视角一致性。
摘要：
本文提出一种用于单目开放集新视角合成（NVS）的方法，该方法利用对象骨架来引导底层扩散模型。在采用预训练2D图像生成器的基线之上，我们的方法利用了Objaverse数据集，该数据集包含具有骨骼结构的动画对象。通过在现有射线条件归一化（RCN）层之后引入骨架引导层，我们的方法提升了姿态准确性和多视角一致性。骨架引导层为生成模型提供详细的结构信息，从而提高合成视角的质量。实验结果表明，我们的骨架引导方法在Objaverse数据集内的多种对象类别上显著增强了一致性和准确性。在不依赖显式3D表示的情况下，我们的方法在定量和定性上均优于现有最先进的NVS技术。
英文摘要：
In this paper, we present an approach for monocular open-set novel view synthesis (NVS) that leverages object skeletons to guide the underlying diffusion model. Building upon a baseline that utilizes a pre-trained 2D image generator, our method takes advantage of the Objaverse dataset, which includes animated objects with bone structures. By introducing a skeleton guide layer following the existing ray conditioning normalization (RCN) layer, our approach enhances pose accuracy and multi-view consistency. The skeleton guide layer provides detailed structural information for the generative model, improving the quality of synthesized views. Experimental results demonstrate that our skeleton-guided method significantly enhances consistency and accuracy across diverse object categories within the Objaverse dataset. Our method outperforms existing state-of-the-art NVS techniques both quantitatively and qualitatively, without relying on explicit 3D representations.
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。
