Using AI to Design Stone Jewelry

arXiv:1811.08759v1 [cs.AI] 21 Nov 2018

Khyatti Gupta, Sonam Damani, Kedhar Nath Narahari{khgupt, sodamani, kedharn}@microsoft.com

Abstract
Jewelry has been an integral part of human culture since ages. One of the mostpopular styles of jewelry is created by putting together precious and semi-preciousstones in diverse patterns. While technology is finding its way in the production
process of such jewelry, designing it remains a time-consuming and involved task.
In this paper, we propose a unique approach using optimization methods coupledwith machine learning techniques to generate novel stone jewelry designs at scale.
Our evaluation shows that designs generated by our approach are highly likeable
and visually appealing.

1Introduction

Jewelry has always been a significant part of human lifestyle and culture. Several styles of jewelry
exist, ranging from bead work to stone setting, each characterized by the variety of materials andtechniques used to make them. In recent years, jewelry manufacturing has made significant progresswith intelligent machinery[8][6]. In the field of jewelry designing, AI is being progressively infusedusing algorithms based on plant modelling[9][10] and Voronoi diagrams[2] to create rubber, steel andbrass jewelry.
Stone jewelry[1] is a conventional form of jewelry that involves setting precious and semi-preciousstones, like Amethyst and Garnet, in uniquely crafted patterns. Setting a combination of stones tolook aesthetically appealing makes creation of stone jewelry a time-consuming and involved process.
The system, described in this paper, models this problem as filling up of a given space by placing
different shapes and sizes together. In this respect, research has been done to place non-overlapping
geometric shapes together[11][7][4][3]. However, such methods tend to optimize on dense packing
rather than visual appeal, and hence are inadequate for creating coherent and aesthetic jewelry designs.
In our work, we leverage jewelry design principles[5] to dynamically choose the most optimal stone
placements for generating visually appealing designs. This is followed by a machine learning model
to prune the unappealing designs. To the best of our knowledge, ours is the first work of applying AI
to create designs for stone jewelry.

2Our Approach

The following section briefly describes our two-fold approach. For the purpose of this paper, we use
105 kinds of stones, each in 7 distinct shapes and 20 distinct sizes.
2.1

Generation

We model the problem of jewelry designing as a packing problem[11], with jewelry shape as thecontainer, stones shapes as objects, and jewelry design principles as the optimization function.
The stones (objects) are placed in jewelry shape (container) one after the other. For placement ofeach stone:
1. We find a candidate set of stones, whose placement shall not potentially create negative
empty space at the end of placement of further stones, using dynamic programming.
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

Table 1: Feature set used for pruning model of jewelry designs
Features
Balance
Emphasis
Harmony of Shape
Harmony of Orientation
Proportion
Unity

Description
Distance between centroid of the jewelry shape and mean of centroids of all stones.
Difference between area occupied by the biggest stone and mean area occupied by the remaining
stones, multiplied by the standard deviation of area occupied by the remaining stones.
Standard deviation of shape vector of the jewelry, where shape vector is the set of counts ofoccurence for each available shape.
Standard deviation of set of orientations of all stones.
Standard deviation of set of area occupied by all stones.
Standard deviation of mean-adjacent-space for all stones, where mean-adjacent-space for a stone
is the mean of space from all of its neighbours.

(a)

(b)

(c)

Figure 1: Designs generated: (a) Process; (b) Good; (c) Bad
2. We rank the candidates using a set of rules based on their size, shape, orientation, and coordinates of the current position. The rules are optimized to maximize harmony, proportion,unity and balance defined in Table 1. For instance, stone with size closer to the mean sizeof the stones that have been placed so far is ranked higher than the one which are farther.
Similarly, a stone is ranked higher if its orientation is radially outward or inward to that ofthe stone placed opposite to it.
3. The candidate stone that is ranked the highest is chosen for the placement.
2.2Pruning

In order to discard the designs which are not as appealing, we use a pruning model. We create adataset of 1200 designs generated using the approach described in Section 2.1, and get it annotatedby 3 judges on the basis of their liking for each design. We use this dataset to train Gradient Boosted
Trees, with features derived using rules adhering to jewelry design principles, as described in Table 1.
For the resultant designs, each stone is surrounded by a silver bezel and filled with textures of realstones. The whole process is visually depicted in Figure 1a.

3

Experimetal Set-Up And Results

A random sample of 100 designs generated using our approach is used for evaluation. In order toaccount for diverse perceptions of beauty, each design is judged by 15 annotators. Also, since thepersonal bias inherently prevents consensual vote per design, the quality is measured for the entire
set as a whole.
It is observed that at least 53% of the designs created by our generative approach are liked by 53%of the annotators. Applying pruning model to these designs further increases both the numbers byanother 7%. Many of the designs generated using our approach are aesthetically appealing, as shownin Figure 1b. However, due to factors like disproportionate placements, lack of harmonic orientations,etc. some designs are not as admirable, as seen in Figure 1c.

4Conclusion

In this paper, we develop a novel approach of creating stone jewelry designs at scale. We leveragejewelry design principles and dynamic programming for generating patterns using stones, followed
by a pruning model to filter out the bad ones. Our approach generates diverse and visually aesthetic
designs that are likeable and readily consumable for production. The last stage in Figure 1a showsa pair of ear rings made by craftsmen in the town of Jaipur, India using one of our designs. In ourfuture work, we plan to make quality improvements by including additional design principles andextending our current work to other forms of jewelry.
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