用手机拍出电影级4D人景重建
过去重建动态3D人景需要几十台相机围一圈拍，现在只用几台低重叠度的相机就能做到。研究者把背景和人分开处理：先用视频扩散模型生成上百个新视角来补全背景，再通过跨视角身份匹配和三角测量精准初始化人体模型，最后用运动自适应模块消除残留伪影。在四个真实数据集上效果领先，还能实现新轨迹渲染和人物替换。虽然目前仍需多台相机，但已大幅降低设备门槛——未来手机多角度拍摄或许就能生成可自由旋转的4D视频。
📄 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.