Splannequin: 通过双检测溅射实现单目人偶挑战视频的冻结3D场景
Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
AI总结：
Splannequin通过双检测溅射技术，提升单目人偶挑战视频冻结3D场景的视觉质量，实现高保真用户可控的冻结时间渲染。
摘要：
从单目人偶挑战（MC）视频合成高保真的冻结3D场景是一个独特的难题，不同于标准的动态场景重建。我们不是专注于建模运动，而是旨在创建一个冻结的场景，同时战略性地保留细微的动态以实现用户可控的即时选择。为此，我们引入了动态高斯溅射的新应用：场景动态建模，保留附近的时序变化，而静态场景则通过固定模型的时间参数来渲染。然而，在这种使用下，单目捕获与稀疏时间监督引入了诸如鬼影和模糊等伪影，这些伪影出现在未被观察或被遮挡的弱监督时间戳的高斯体中。我们提出了Splannequin，一种架构无关的正则化方法，可以检测高斯原始体的两种状态，隐藏和缺陷，并应用时间锚定。在主要向前相机运动下，隐藏状态被锚定到其最近的良观测过去状态，而缺陷状态被锚定到具有更强监督的未来状态。我们的方法通过简单的损失项整合到现有的动态高斯管道中，不需要架构更改，并且添加了零推理开销。这导致了显著改善的视觉质量，使高保真的用户可选择的冻结时间渲染成为可能，通过96%的用户偏好验证。项目页面：https://chien90190.github.io/splannequin/
英文摘要：
Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/
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。
