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Capturing general deforming scenes from monocular RGB video is crucial formany computer graphics and vision applications. However, current approachessuffer from drawbacks such as struggling with large scene deformations,inaccurate shape completion or requiring 2D point tracks. In contrast, ourmethod, Ub4D, handles large deformations, performs shape completion in occludedregions, and can operate on monocular RGB videos directly by usingdifferentiable volume rendering. This technique includes three new in thecontext of non-rigid 3D reconstruction components, i.e., 1) A coordinate-basedand implicit neural representation for non-rigid scenes, which in conjunctionwith differentiable volume rendering enables an unbiased reconstruction ofdynamic scenes, 2) a proof that extends the unbiased formulation of volumerendering to dynamic scenes, and 3) a novel dynamic scene flow loss, whichenables the reconstruction of larger deformations by leveraging the coarseestimates of other methods. Results on our new dataset, which will be madepublicly available, demonstrate a clear improvement over the state of the artin terms of surface reconstruction accuracy and robustness to largedeformations.

Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model