DNN ( Digital Native NFTs )

This is a PoC to introduce a novel approach to creating NFTs. The aim is to have every NFT derived from only geometric objects. You can use these geometric elements to draw anything from cars to birds

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  • Track 1: Best use case for real-world adoption

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Description

1. Short description 

The aim of this approach is that the objects drawn are alive digitally.


2. Motivation behind the project 

Most of the current NFTs are from the real world. They have no actual presence in the digital world. The properties that they possess are not native to the setting in which they are portrayed i.e. the digital setting. C

Cartesi enables us to use existing tools: p5.js, Puppeteer.

3. Detailed description - 

Tools

To generate our drawings (i.e. DNNs ), we will need two tools:

  • A Javascript client-side library called p5.js for creating visual media. This library is based on the Processing software, an industry-standard software used by visual artists.
  • A local Javascript server. Many options to choose from. You can use Live Server extension for VS Code. Here's how you can set it up.
  • Puppeteer. A Nodejs library that will enable us run a headless browser in at the backend (node)

In case for some reason you don't want to set up the first two on your local environment, you can always use the p5.js web editor.

Design

Fractionalization

First, let's begin with the whole. Each drawing is encapsulated within a canvas. A canvas defines the size of the drawing i.e. its bounds.

Within a canvas, we have a drawing(s). A drawing can be split into fractions by the creator.

A fraction is a discrete entity within a drawing.

Ownership

A fraction can be owned by at most 1 account. A drawing can be owned by more than one account if it has at least 2 fractions. Intersection

Fractionalization presents some challenges with ownership, particularly when it comes to asset transfer. How do we know that the fractionated part isn't a part of another fraction?

You can equate this problem to double-spending in fungible tokens. To ensure malicious creators aren't reselling the same fraction, validators check that there's no area intersection of the fraction with any other fraction.

Interactivity

Two or more DNNs can be merged in a deterministic way. Possible applications for this are collaborative art and consolidation of assets.

Note: The creators/collectors need to use their discretion while making such combinations.


4. Challenges faced 

It was an exciting journey! I mean the challenges.

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