DORSal：面向场景的对象中心化表示的扩散模型
DORSal: Diffusion for Object-centric Representations of Scenes et al
AI总结：
本文提出DORSal，将视频扩散架构与冻结的对象中心化槽位表示结合，在保持对象级编辑能力的同时实现高保真可扩展3D场景渲染，并在合成与真实大规模数据集上优于现有方法。
摘要：
近年来，3D场景理解的进展使得能够在大规模多样化场景数据集上可扩展地学习表示。因此，对未见场景和对象的泛化、仅从单张或少量输入图像渲染新视角，以及支持编辑的可控场景生成，如今已成为可能。然而，与NeRF等单场景优化模型相比，在大量场景上联合训练通常会降低渲染质量。在本文中，我们利用扩散模型的最新进展，赋予3D场景表示学习模型渲染高保真新视角的能力，同时在很大程度上保留对象级场景编辑等优势。具体而言，我们提出DORSal，它将视频扩散架构适配用于以冻结的、基于对象中心化槽位的场景表示为条件的3D场景生成。在复杂的合成多对象场景和真实世界大规模Street View数据集上，我们表明DORSal能够实现具有对象级编辑能力的可扩展3D场景神经渲染，并优于现有方法。
英文摘要：
Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on frozen object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches.
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。
