SINGAPO：单图像控制生成物体中的关节部件
SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
AI总结：
针对单张图像生成家用关节式物体3D资产的难题，提出基于扩散模型的由粗到精生成方法，在真实感、相似度和重建质量上均大幅优于现有技术。
摘要：
我们解决了从单张图像创建家用关节式物体3D资产的挑战。此前的关节式物体创建工作要么需要多视角多状态输入，要么仅能对生成过程进行粗略控制。这些限制阻碍了关节式物体建模的可扩展性与实用性。在本研究中，我们提出了一种从单张图像生成关节式物体的方法。从任意视角观察静止状态的物体，我们的方法能生成与输入图像视觉一致的关节式物体。为捕捉单视角物体带来的部件形状与运动的模糊性，我们设计了一个扩散模型，用于学习物体在几何和运动学层面的合理变化。为解决生成具有多领域属性的结构化数据的复杂性，我们设计了一条从高层结构到几何细节、以由粗到精方式生成关节式物体的流水线，其中使用部件连接图和部件抽象作为代理。我们的实验表明，在生成物体的真实感、与输入图像的相似度以及重建质量方面，我们的方法大幅优于当前最先进的关节式物体创建方法。
英文摘要：
We address the challenge of creating 3D assets for household articulated objects from a single image. Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process. These limitations hinder the scalability and practicality for articulated object modeling. In this work, we propose a method to generate articulated objects from a single image. Observing the object in resting state from an arbitrary view, our method generates an articulated object that is visually consistent with the input image. To capture the ambiguity in part shape and motion posed by a single view of the object, we design a diffusion model that learns the plausible variations of objects in terms of geometry and kinematics. To tackle the complexity of generating structured data with attributes in multiple domains, we design a pipeline that produces articulated objects from high-level structure to geometric details in a coarse-to-fine manner, where we use a part connectivity graph and part abstraction as proxies. Our experiments show that our method outperforms the state-of-the-art in articulated object creation by a large margin in terms of the generated object realism, resemblance to the input image, and reconstruction quality.
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。
