We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a geometrically and temporally consistent solution to this underconstrained problem: the depth predictions of corresponding points across frames should induce plausible, smooth motion in 3D. We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to impose multi-view consistency constraints with wide baselines. We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars), as well as camera motion. Our depth maps give rise to a number of depth-and-motion aware video editing effects such as object and lighting insertion.
翻译:我们提出一种方法来估计动态场景的深度,其中含有任意移动的物体,从用移动相机拍摄的普通视频中估算。我们寻求以几何和时间上一致的方式解决这个不受限制的问题:对各框架对应点的深度预测应引致三维运动的光滑。我们在一个新的试验时间培训框架中提出这一目标,在这种框架中,有线新闻网的深度预测与整个输入视频的辅助场景流预测MLP一起进行训练。我们通过在不同的时间步骤对场景流预测 MLP进行循环地解动,我们计算出短距离场流以直接在3D中进行局部平稳运动,而长距离场流则对宽基线进行多视一致性限制。我们对包含各种移动物体(物体、人、汽车)以及摄像机运动的各种具有挑战性的视频显示准确和时间一致的结果。我们的深度地图产生了一些深度和移动感知的视频编辑效应,例如对象和照明插入。