Automated Prop Placement Using Machine Learning
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Solution Overview
Problem
The manual placement of props in virtual worlds within computer and video games is time-consuming and often results in unrealistic or distracting arrangements, as the sheer number of props and maps requires extensive human intervention, while automated solutions using simple rules may fail to produce acceptable results due to variation among maps and props.
Innovation Solution
An automated prop placement tool utilizing a trained machine learning mechanism that applies spatial rules, prop-specific rules, prop-to-fixed-object distances, and prop-to-prop distances to suggest optimal placements, combining these factors to generate a placement score for each prop on a target map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual placement of props is performed by skilled graphic artists, then the quality and realism of prop arrangements is improved, but the time and effort required increases enormously
Solution Approach 1:
The system enables automated prop placement by training a machine learning model on example maps with ideal prop arrangements. The trained model then autonomously determines optimal prop placements for new maps without requiring manual intervention, allowing the system to serve itself in generating realistic prop arrangements.
Solution Approach 2:
The machine learning model is trained in advance on a dataset of example maps that contain ideal prop placements. This preliminary training phase allows the model to learn spatial relationships and placement patterns, which are then applied automatically when new maps are processed, eliminating the need for manual placement while maintaining high quality results.
2Productivity
If automated prop placement using simple rules is implemented, then the time required is reduced, but the quality of prop arrangements deteriorates to distracting and unrealistic results
Solution Approach 1:
The patent replaces simple rule-based automated systems with a machine learning model that has been trained on example maps. This substitution allows the system to maintain high productivity through automation while achieving high-quality prop arrangements by learning from trained data rather than following rigid rules that cannot handle variation among maps and props.
Solution Approach 2:
The system changes from using fixed simple rules to using a trained machine learning model that can adapt its behavior based on the specific characteristics of each map and prop combination. This parameter change enables the automated system to produce realistic results by learning optimal placement strategies from training data.
3Adaptability or versatility
If the number of scenes and maps in virtual worlds increases, then the scope and complexity of virtual worlds expands, but the time required for prop placement increases exponentially
Solution Approach 1:
The trained machine learning model autonomously processes maps and generates prop placements without human intervention. This self-service capability allows the system to handle large numbers of maps and scenes efficiently, scaling to accommodate expanding virtual worlds without proportionally increasing manual labor time.
Solution Approach 2:
By performing the time-consuming training phase in advance on a dataset of example maps, the system prepares a reusable model that can quickly process new maps. This preliminary action enables the system to handle large volumes of prop placement tasks efficiently, as the model can be applied repeatedly without requiring manual placement for each new map.
Data Source
AI summary
Techniques are described herein for facilitating the placement of props on maps by an automated prop placement tool that makes use of a trained machine learning mechanism. The machine learning mechanism is trained based on one or more training maps upon which props have been placed. The machine learning mechanism may be trained to suggest placement based on (a) spatial rules relating, (b) prop-specific rules, (c) prop-to-fixed-object distances between props and map structures, and (d) distances between props. Once the machine learning mechanism is trained, the prop placement tool may be provided as input (a) map data that defines a target map and (b) prop data that specifies the set of target props to be placed on the target map. Based on this input and the machine learning mechanism's trained model, the prop placement tool outputs a suggested placement, for each of the target props, on the target map.