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Appendix
D Complexity Filtering
As mentioned in Section 4, while testing our method alongside baseline methods, we reached ceiling performance for all our methods. Ellis et al. [11] got around this by creating a “hard” test case by sampling more objects. For us, when we increased the number of objects to increase complexity, we saw that it increased the probability that a large object would be sampled and subtract from the whole scene, resulting in simpler scenes. This is shown by Figure 11(b), which is our training distribution. Even though we sample a large number of objects, the scenes don’t look visually interesting. When we studied the implementation details of Ellis et al. [11], we noticed that during random generation of expressions, they ensured that each shape did not change more that 60% or less than 10% of the pixels in the scene. Instead of modifying our tree sampling method, we instead chose to rejection sample based on the compressibility of the final rendered image.
Authors:
(1) Shreyas Kapur, University of California, Berkeley ([email protected]);
(2) Erik Jenner, University of California, Berkeley ([email protected]);
(3) Stuart Russell, University of California, Berkeley ([email protected]).
This paper is
