Nov. - Dec. 2022

• 122 ideas from “State of the Art of Neural Rendering” (2020) by Ayush Tewari, Ohad Fried,  Justus Thies, et al. 

For 8 weeks, I used this 2020 review on Neural Rendering and the subsequent 2022 version to introduce myself to the intersection of graphics, vision, and learning. Instead of continuing to store my mnemonics in the voice memos app, I began to wonder about what it would take to create and store mnemonics in 2D or 3D without having animation skills or a team of artists. Towards the goal of creating machines that learn and think like people, might it be useful to think about how humans make memory palaces about non-trivial content? If not, then I've had a lot of fun & I've ruminated on surveys of what already exists.

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- "the goal of neural rendering is to generate photo-realistic imagery in a controllable way"

-  "while there exists some work on generating neural scene representations, there is less progress on designing neural operators that take neural scene representations as input"

- "to 'learn less and know more' by incorporating differentiable physics simulators"