Splannequin：基于双重检测渲染的单目人体模型挑战视频冻结技术

摘要

从单目人体模型挑战（MC）视频中合成高保真静态3D场景是一个与标准动态场景重建截然不同的独特问题。我们的目标并非模拟运动，而是创建凝固场景的同时策略性保留细微动态，以实现用户可控的瞬时选择。为此，我们创新性地应用动态高斯泼溅技术：通过动态建模保留邻近时间域的细微变化，再固定模型时间参数渲染静态场景。然而在此方案下，单目采集与稀疏时间监督会导致高斯元素在弱监督时间点出现不可见或被遮挡，从而产生重影和模糊等伪影。我们提出Splannequin——一种与架构无关的正则化方法，通过检测高斯图元的隐藏态与缺陷态并实施时间锚定。在相机主要前向运动下，隐藏态会锚定至近期被充分观测的过去状态，而缺陷态则锚定至具有更强监督的未来状态。该方法通过简单损失项即可融入现有动态高斯流程，无需改变架构且不增加推理开销，最终实现视觉质量显著提升，生成可供用户选择冻结时间的高保真渲染效果，用户偏好度达96%。项目页面：https://chien90190.github.io/splannequin/

English

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/

Splannequin：基于双重检测渲染的单目人体模型挑战视频冻结技术

Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting

摘要

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