4D人景重建：稀疏相机也能拍出电影级效果
过去，要捕捉动态人物的4D影像（三维+时间），需要几十台相机围成一圈。但现实中往往只有几台相机，拍出来的人像残缺、背景模糊。这篇研究反其道而行：先用AI生成数百个虚拟视角来补全背景，再通过跨视角身份匹配和关键点拟合，让人物在稀疏视角下也能稳定重建。最后用运动一致性模块消除残留伪影。在四个真实数据集上，它的新视角合成效果达到当前最优。你明天用不上它，但它意味着未来用手机拍几段视频，就能生成可自由旋转的4D人物场景——电影特效、虚拟现实的门槛正在降低。
📄 原文摘要(英文)
Existing volumetric capture of dynamic human performance achieves high fidelity with dense camera arrays. However, in real-world scenarios, only a handful of low-overlap cameras are available, which degrades the output quality and leaves large areas unobserved. Recent 4D reconstruction methods have focused on low-overlap settings, yet they still produce noticeable artifacts in under-observed regions. Video diffusion models have emerged as another option, but they show geometrically inconsistent results for humans. To address these limitations, we propose StudioRecon, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans. We densify background supervision by synthesizing hundreds of camera-controlled novel views with a video diffusion model. We also robustly initialize deformable Gaussian humans with cross-view identity association and triangulated multi-view keypoint fitting. Finally, our recursive enhancement module with motion-adaptive consistency injection harmonizes the composed output, thereby further avoiding remaining artifacts. We achieve state-of-the-art novel view synthesis across four real-world datasets and demonstrate applications such as novel trajectory rendering and human replacement.