Image Processing for Neural Terrain-Adaptive Digital Character Poses
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Solution Overview
Problem
Existing methods for driving digital characters, such as behavior trees, state machines, and motion matching, result in fixed behaviors that lack flexibility, are not smooth in transitioning, and have low realism due to poor terrain adaptation, requiring high costs and expertise for design.
Innovation Solution
An image processing method using neural networks and attention mechanisms to generate drive instructions based on terrain features, enabling digital characters to autonomously navigate complex terrains by predicting and optimizing motion paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If preconfigured behavior trees, state machines, or animation databases are used to drive digital characters, then the character behavior is fixed and easy to implement, but the behavior lacks flexibility and smooth transitions
Solution Approach 1:
The patent transforms fixed, preconfigured behavior systems into dynamic, adaptive systems by using neural networks that process terrain data in real-time. The character's behavior is no longer static but dynamically adjusted based on environmental inputs, allowing smooth transitions between actions while maintaining ease of implementation through automated learning.
Solution Approach 2:
The invention changes the parameters of the behavior system from fixed discrete states to continuous probabilistic distributions. By using neural networks to output continuous action probabilities rather than discrete preconfigured behaviors, the system achieves both flexibility and smooth transitions while remaining computationally efficient.
2Ease of manufacture
If preconfigured behavior trees, state machines, or animation databases are used to drive digital characters, then the implementation is simple, but the action has low realism due to poor terrain adaptation
Solution Approach 1:
The patent introduces neural networks as an intermediary between the simple navigation path input and the complex terrain adaptation output. This intermediary processes terrain height data and generates realistic character poses that adapt to the environment, bridging the gap between simple implementation and high realism.
Solution Approach 2:
The invention replaces traditional mechanical behavior systems (behavior trees, state machines) with a data-driven neural network system. This substitution allows the character to adapt to terrain through learned patterns rather than rigid preconfigured rules, achieving realistic terrain adaptation while maintaining implementation simplicity.
3Reliability
If behavior trees, state machines, or animation databases are designed and produced, then high-quality character control is achieved, but the design and production costs are high
Solution Approach 1:
The patent enables the system to generate high-quality character control automatically through neural network training, eliminating the need for manual design of behavior trees, state machines, or animation databases. The system serves itself by learning from terrain data and navigation paths, significantly reducing design complexity while maintaining control quality.
Solution Approach 2:
The invention uses neural networks to copy and generalize motion patterns from training data, allowing the character to reproduce realistic behaviors without requiring explicit programming of each scenario. This copying mechanism achieves high-quality control with minimal design effort.
Data Source
AI summary
This application provides an image processing method, including: obtaining first data, where the first data corresponds to a first scene model, a first character, and a first location of the first character in the first scene model; and generating N first images based on the first data, where the N first images are in one-to-one correspondence with N second locations, an nth first image is used for presenting a pose of the first character at an nth second location, and the pose of the first character at the nth second location corresponds to a terrain feature of the first scene model at the nth second location. According to embodiments of this application, the pose of the first character corresponds to the terrain feature of the first scene model, so that the first character can automatically avoid an obstacle in a complex terrain (for example, a three-dimensional terrain).