用3D高斯点云实现稀疏视角下逼真的人物物体交互渲染
Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting
- 将人和物体统一为动态3D高斯表示，直接优化几何一致性
- 在稀疏视角下实现顶尖渲染质量，且保持高效计算
- 适合需要物理合理交互的虚拟试穿、数字人等场景
从稀疏视角渲染逼真的人物-物体交互（HOI）是一项具有挑战性但至关重要的任务。现有方法难以同时兼顾高渲染质量、物理合理性与计算效率。为此，我们提出HOGS（基于3D高斯点云的人物-物体交互渲染），一种从稀疏视角出发的高效框架，通过动态3D高斯表示建模人与物体，并引入新型优化过程，直接在高斯点上施加几何一致性约束（如防止穿透或漂浮接触），以实现物理合理性。为应对稀疏视角下的不确定性，框架融合两个预训练模块：优化引导的人体姿态精修器，用于遮挡下的鲁棒姿态估计；以及人体-物体接触预测器，可高效识别交互区域，指导新的接触与分离损失。在人体-物体及手-物体交互数据集上的大量实验表明，HOGS在渲染质量上达到当前最优水平，同时保持高计算效率。
原文摘要 · Abstract (English)
Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D Gaussian Splatting), a novel framework for efficient HOI rendering with physically plausible geometric constraints from sparse views. HOGS represents both humans and objects as dynamic 3D Gaussians. Central to HOGS is a novel optimization process that operates directly on these Gaussians to enforce geometric consistency (i.e., preventing inter-penetration or floating contacts) to achieve physical plausibility. To support this core optimization under sparse-view ambiguity, our framework incorporates two pre-trained modules: an optimization-guided Human Pose Refiner for robust estimation under sparse-view occlusions, and a Human-Object Contact Predictor that efficiently identifies interaction regions to guide our novel contact and separation losses. Extensive experiments on both human-object and hand-object interaction datasets demonstrate that HOGS achieves state-of-the-art rendering quality and maintains high computational efficiency.
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