We explore the task of embodied view synthesis from monocular videos of deformable scenes. Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from in-scene motion of actors: (1) egocentric cameras that simulate the point of view of a target actor and (2) 3rd-person cameras that follow the actor. Building such a system requires reconstructing the root-body and articulated motion of each actor in the scene, as well as a scene representation that supports free-viewpoint synthesis. Longer videos are more likely to capture the scene from diverse viewpoints (which helps reconstruction) but are also more likely to contain larger motions (which complicates reconstruction). To address these challenges, we present Total-Recon, the first method to photorealistically reconstruct deformable scenes from long monocular RGBD videos. Crucially, to scale to long videos, our method hierarchically decomposes the scene motion into the motion of each object, which itself is decomposed into global root-body motion and local articulations. To quantify such "in-the-wild" reconstruction and view synthesis, we collect ground-truth data from a specialized stereo RGBD capture rig for 11 challenging videos, significantly outperforming prior art. Code, videos, and data can be found at https://andrewsonga.github.io/totalrecon .

本文提出了Total-Recon方法，用于从长时间单目RGBD视频中重建变形场景并进行自由视点合成，通过场景运动层次分解，实现了快速高效的实现，并最终超越以往的方法。

Total-Recon： 嵌入式视角合成的可变形场景重建

Total-Recon：嵌入式视角合成的可变形场景重建