in my view.
Potential weaknesses (if thereâs space, please comment on the following in the rebuttal):
W1. Loss (5) seems to have a degeneracy when v==gu, i.e., p(v|u) term is ignored. While this doesnât seem to affect the results much, Iâm wondering if the formulation could be slightly improved by adding a constant epsilon so that itâs (||v-gu||^\gamma + epsilon) * p(v|u).
W2. L170-172 mentions that the learning formulation can be modified so that knowledge of g is not required. I find having g in the formulation somewhat unsatisfying, so Iâm wondering if you can say a bit more about this possibility.
W3. The shown examples are approximately planar scenes. It might be nice to include another toy example of a 3D object, e.g., textured sphere or other closed 3D object.
Minor comments/suggested edits:
+ There are a number of small typos throughout; please pass through a spell+grammar checker.
+ Equation after L123 - It isn't clear what alpha is on first read, and is described later in the paper. Maybe introduce alpha here.
+ L259 mentions that the approach simply observes a large dataset of unlabelled images. However, flow is also required. Perhaps rephrase.
+ L259 mentions that the approach simply observes a large dataset of unlabelled images. However, flow is also required. Perhaps rephrase.