标题： BulletGen：通过子弹时间生成改进4D重建 显示英文标题

标题： BulletGen: Improving 4D Reconstruction with Bullet-Time Generation

摘要： 将随意拍摄的单目视频转换为完全沉浸式的动态体验是一个高度不适定的任务，伴随着重大的挑战，例如，重建未看到的区域，以及处理单目深度估计中的歧义性。 在本工作中，我们引入了BulletGen，一种利用生成模型来纠正基于高斯的动态场景表示中的错误并补全缺失信息的方法。 这是通过将基于扩散的视频生成模型的输出与单个冻结的“子弹时间”步骤中的4D重建对齐来实现的。 然后，生成的帧用于监督4D高斯模型的优化。 我们的方法无缝地将生成内容与静态和动态场景组件结合，在新视角合成以及2D/3D跟踪任务中取得了最先进结果。 显示英文摘要

摘要： Transforming casually captured, monocular videos into fully immersive dynamic experiences is a highly ill-posed task, and comes with significant challenges, e.g., reconstructing unseen regions, and dealing with the ambiguity in monocular depth estimation. In this work we introduce BulletGen, an approach that takes advantage of generative models to correct errors and complete missing information in a Gaussian-based dynamic scene representation. This is done by aligning the output of a diffusion-based video generation model with the 4D reconstruction at a single frozen "bullet-time" step. The generated frames are then used to supervise the optimization of the 4D Gaussian model. Our method seamlessly blends generative content with both static and dynamic scene components, achieving state-of-the-art results on both novel-view synthesis, and 2D/3D tracking tasks.