Abstract
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: this https URL
Abstract (translated)
合成高保真度的冻结3D场景，这是从单目Mannequin-Challenge（MC）视频中提取的独特问题，这个问题与标准动态场景重建不同。在我们的目标中，并非专注于建模运动本身，而是要创建一个静止场景，同时战略性地保留微妙的动力学现象，以实现用户控制的即时选择。为了实现这一目标，我们引入了一种新型的动态高斯点绘制应用：通过动态模型来保持附近的时间变化，并通过固定模型的时间参数来渲染静态场景。然而，在这种使用方式下，单目捕捉加上稀疏时间监督会在Gaussians（在弱监督时间戳处未被观察到或被遮挡）中引入如幽灵和模糊等伪影。 我们提出了Splannequin——一种与架构无关的正则化方法，它可以检测高斯元素的两种状态：隐藏态和缺陷态，并应用时间锚定。在主要向前移动摄像头的情况下，隐藏的状态会被锚定到其最近且观察良好的过去状态，而缺陷的状态则被锚定到未来监督更强烈的状态。 我们的方法通过简单的损失项融入现有的动态高斯管道中，无需架构更改，并且不会增加任何推理开销。这导致了显著的视觉质量改进，能够实现高保真度、用户可选择的时间冻结渲染，经验证据是96%的用户偏好。项目页面：[此处插入链接]（请根据实际情况添加正确的URL）。
URL
https://arxiv.org/abs/2512.05113
