Conditional Inverse Kinematics for Realistic Game Character Motion
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Solution Overview
Problem
Current techniques for generating realistic motion in electronic games are time-consuming and labor-intensive, requiring substantial effort from designers to adjust character models and often lack the realism of real-world movements, especially when using pre-defined templates or motion capture methods which are limited in flexibility and cost-effective for a large number of characters.
Innovation Solution
The use of machine learning techniques, specifically generative models like autoencoders and conditional variational autoencoders, to analyze and learn representations of human poses from real-life video data, allowing for the automated generation of realistic motion for in-game characters by encoding pose information into a latent feature space and generating new poses through sampling and blending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-tuned techniques are used to adjust skeleton positions for realistic movement, then movement realism is improved, but design complexity and time consumption increase substantially
Solution Approach 1:
The patent replaces the manual mechanical adjustment process (hand-tuned skeleton positioning) with an automated machine learning system. The neural network automatically generates skeleton positions and joint configurations based on motion capture data, eliminating the need for designers to manually adjust each skeleton element while maintaining movement realism.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously generate character movements without requiring continuous human intervention. Once trained on motion capture data, the model independently produces realistic movements for multiple characters, reducing the ongoing design workload.
2Productivity
If motion capture methods are used to reduce design complexity, then design time is reduced, but flexibility and cost-effectiveness decrease due to requirements for actors and complex capture stages
Solution Approach 1:
The patent uses motion capture data as training examples to teach the neural network desired movement patterns. Instead of directly applying captured movements to multiple characters (which would require repeating expensive capture sessions), the system copies the underlying movement principles learned from the training data and applies them generatively to create varied movements for numerous characters.
Solution Approach 2:
The system changes parameters by using the neural network to generate continuous variations of movements based on the learned patterns. The model can adjust movement parameters such as speed, amplitude, and timing to create diverse character animations from a single set of motion capture training data, enhancing flexibility without additional capture sessions.
3Productivity
If pre-defined movement templates are used to reduce design time, then productivity increases, but movement realism decreases compared to real-world movements
Solution Approach 1:
The patent transitions from static pre-defined templates to dynamic generated movements. The neural network produces movements that adapt to specific character models, terrains, and game contexts in real-time, creating realistic variations rather than repeating fixed template patterns. This dynamic generation maintains high design speed while improving movement realism.
Data Source
AI summary
Systems and methods are provided for enhanced pose generation based on conditional modeling of inverse kinematics. An example method includes accessing an autoencoder trained based on poses, with each pose being defined based on location information of joints, and the autoencoder being trained based on conditional information indicating positions of a subset of the joints. The autoencoder is trained to reconstruct, via a latent variable space, each pose based on the conditional information. Information specifying positions of the subset of the joints is obtained via an interactive user interface and the latent variable space is sampled. An output is generated for inclusion in the interactive user interface based on the sampling and the positions.