组合式三维人-物神经动画
Compositional 3D Human-Object Neural Animation
AI总结：
本文提出基于隐式神经表示与CC-NeRF的组合式三维人-物神经动画框架，通过潜码解耦人物和物体，实现新颖交互、人物、物体及姿态驱动下的HOI渲染与泛化控制。
摘要：
人-物交互（HOI）对于以人为中心的视觉生成、AR/VR和机器人技术等以人为中心的场景理解应用至关重要。由于现有方法主要探索捕捉HOI，渲染HOI仍然研究较少。本文从组合视角应对HOI动画中的这一挑战，即由新颖姿态序列驱动，为包含新颖交互、新颖人物和/或新颖物体的新颖HOI制作动画。具体而言，我们采用神经人-物形变，基于隐式神经表示对HOI动态进行建模和渲染。为了使交互姿态能够在不同人物和物体之间迁移，我们随后设计了一种新的组合条件神经辐射场（即CC-NeRF），它使用潜码分解人与物体之间的相互依赖，以实现新颖HOI的组合式动画控制。实验表明，所提出的方法能够很好地泛化到各种新颖HOI动画设置。我们的项目页面是https://zhihou7.github.io/CHONA/。
英文摘要：
Human-object interactions (HOIs) are crucial for human-centric scene understanding applications such as human-centric visual generation, AR/VR, and robotics. Since existing methods mainly explore capturing HOIs, rendering HOI remains less investigated. In this paper, we address this challenge in HOI animation from a compositional perspective, i.e., animating novel HOIs including novel interaction, novel human and/or novel object driven by a novel pose sequence. Specifically, we adopt neural human-object deformation to model and render HOI dynamics based on implicit neural representations. To enable the interaction pose transferring among different persons and objects, we then devise a new compositional conditional neural radiance field (or CC-NeRF), which decomposes the interdependence between human and object using latent codes to enable compositionally animation control of novel HOIs. Experiments show that the proposed method can generalize well to various novel HOI animation settings. Our project page is https://zhihou7.github.io/CHONA/
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。
