Garden – Spark AR

Most filters released for Instagram, Facebook, or Snapchat are meant to be short mostly shallow experiences. There seems room for filters that are meant to be returned to. What about an experience that could remember your progress, even when you leave it?

The Premise

In stepped Garden, an AR garden where you can plant up to 24 different tulips at a time and they will grow while you are away. Each color tulip costs and yields different amounts of coins, but they also take different amounts of time to grow. This filter is truly a game that encourages you to return to plant new colors of flowers and reinvest them to grow your dream garden.

The Challenge of Evergreen

There were many challenges with this filter. The first being the storage of information. The persistence module of Spark AR allows developers the ability to store information between sessions and there had been a few filters that stored some information. No filter, as far as we have seen, was as extensive with information storage as Garden. With 24 different flowers and their respective colors, state of growing, rotations, and a function tutorial all stored, Garden pushes the boundaries of what a filter can do.

The next challenge was keeping to the spark guidelines while educating the user on necessary information for the filter. We achieved this by using almost no text and opted for visual representations on the farm board and with the coin bag, making accessible the buy/sell prices, the time it takes to grow a flower, and how much money you have. Finally, optimization was huge in this filter. In order to make the filter as accessible as possible, the suggested size limit for filters on Instagram and Facebook is 2 Mb. With all 24 flowers, the surrounding meshes, a 30 looping sound clip, and UI elements, the total size for this filter is only 1.9 Mb making it very accessible.

Pushing the Boundaries

Garden was a challenge in code and pushing the boundaries on what is possible within a Spark AR filter. With Garden’s first version released, we are poised to create filters that can go beyond the average interactivity and keep users returning to the content.