Photogrammetry works exceedingly well because the depth maps that they generate are quite precise and accurate, and mesh reconstruction usually assumes that these points are quite close to ground truth.
Deep learning approaches usually have medium accuracy but low precision, which causes the flickering and smooth surfaces that you see on the person. Even the background has flickering despite being computed through stereo, likely because the camera motion is primarily forward-backward (vs. more accurate side-to-side motion), the baseline is likely small, and the depth isn't globally optimized.
This type of research is super great for applications requiring lower accuracy, typically visual-only applications (e.g. selective blurring, faking stereo on a frame, etc.). But as an input to photogrammetry — probably not anytime soon, until the problems above get resolved.