Neural Network Intermediate State Identification for Visual Objects
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Solution Overview
Problem
Existing systems for virtual environments, such as games, face challenges in identifying intermediate states of visual objects in response to user inputs within a predetermined time, affecting the quality and responsiveness of the service provided.
Innovation Solution
An electronic device uses a neural network to receive user inputs, identify intermediate states for transitioning between visual object states, and determine compensation values based on whether the transition occurs within a set time, training the neural network to enhance the plausibility and responsiveness of state changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is used to identify intermediate states of visual objects in response to user inputs, then the accuracy and plausibility of state identification is improved, but the time required for processing and identifying states within the predetermined time limit deteriorates
Solution Approach 1:
The system pre-identifies candidate intermediate states between the first state and second state before the user input is processed. By having candidate states prepared in advance, the neural network only needs to evaluate which candidate is most appropriate rather than generating states from scratch during the time-critical moment, thus improving both accuracy and response time
Solution Approach 2:
The state transition process is divided into distinct stages: identifying candidate intermediate states, evaluating them using the neural network, and selecting the final state. This segmentation allows each stage to be optimized independently, with candidate generation happening beforehand and neural network evaluation focused only on selecting from pre-identified options, reducing overall processing time while maintaining accuracy
2Productivity
If the system ensures state transitions occur within a predetermined time limit, then the responsiveness of the virtual environment is improved, but the accuracy and plausibility of identifying appropriate intermediate states deteriorates
Solution Approach 1:
Candidate intermediate states are identified and prepared in advance before the time limit becomes critical. This preliminary identification ensures that when user input occurs, the system already has plausible candidate states ready, allowing the neural network to quickly evaluate them without compromising the predetermined time limit or the plausibility of the transition
Solution Approach 2:
The neural network acts as an intermediary that evaluates pre-identified candidate states rather than generating states from scratch. This intermediary role allows the system to leverage pre-computed candidate states (ensuring plausibility) while the neural network provides rapid evaluation (ensuring responsiveness within time limits)
Data Source
AI summary
A non-transitory computer readable storage medium store one or more programs including instructions causing an electronic device to receive, while a visual object is in a first state among a plurality of states, a user input for switching a state of the visual object to a second state among the plurality of states; provide data regarding the user input as input data to a neural network for training of the neural network; identify a third state among the plurality of states, wherein the third state is an intermediate state for switching the first state to the second state; obtain, from the neural network, data regarding the third state as output data for the data regarding the user input; determine a compensation value for the data regarding the third state; and train, by providing the data regarding the compensation value to the neural network, the neural network.