Weakness 1. The setup of capturing strategy is complicated and is not easy for applications in real life. To initialize the canonical space, the first stage is to capture the static state using a moving camera. Then to model motions, the second stage is to capture dynamic states using a few (4) fixed cameras. Such a 2-stage capturing is not straightforward. 2. To utilize a volumetric representation in the deformation field is not a novel idea. In the real-time dynamic reconstruction task, VolumeDeform [1] has proposed volumetric grids to encode both the geometry and motion, respectively. 3. The quantitative experiments (Tab. 2 and Tab. 3) show that the fidelity of rendered results highly depends on the 2-stage training strategy. In a general capturing case, other methods can obtain more accurate rendered images. Oppositely, Tab. 2 shows that it is not easy to fuse the designed 2-stage training strategy into current mainstream frameworks, such as D-NeRF, Nerfies and HyperNeRF. It verifies that the 2-stages training strategy is not a general design for dynamic NeRF.
[1] Innmann, Matthias, Michael Zollhöfer, Matthias Nießner, Christian Theobalt, and Marc Stamminger. "Volumedeform: Real-time volumetric non-rigid reconstruction." In European conference on computer vision (ECCV), pp. 362-379. Springer, Cham, 2016.
2. To utilize a volumetric representation in the deformation field is not a novel idea. In the real-time dynamic reconstruction task, VolumeDeform [1] has proposed volumetric grids to encode both the geometry and motion, respectively.