Generative Adversarial Network for Realistic Character Animation
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Solution Overview
Problem
Conventional digital animation systems suffer from inaccuracies, inefficiencies, and inflexibilities in generating complex animation sequences, often producing unrealistic or clumsy animations that require extensive computational resources and specific instructions, limiting their ability to create varied and creative movements.
Innovation Solution
The use of a generative adversarial network and a hip motion prediction network within a convolutional neural network framework allows for the generation of realistic and complex animation sequences using a sparse set of keyframes or random code vectors, enabling flexible and efficient creation of animations such as dance or martial arts sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional systems interpolate between joint rotations, then animations can be generated without high-density key frames, but the generated animations are unrealistic
Solution Approach 1:
The patent replaces conventional mechanical interpolation methods with a neural network-based system. The neural network learns realistic motion patterns from training data and generates joint rotations that produce realistic animations without requiring high-density key frames, thus substituting mechanical interpolation with intelligent synthesis.
Solution Approach 2:
The patent changes the approach from direct joint rotation interpolation to using high-level parameters (such as motion descriptors or key frame positions) that guide the neural network. This parameter transformation allows the system to generate realistic animations with fewer constraints, improving both ease of manufacture and animation quality.
2Manufacturing precision
If animators set joint poses for individual frames, then animation accuracy can be improved, but the process becomes time-consuming and computationally expensive
Solution Approach 1:
The neural network system performs animation generation autonomously without requiring animator intervention for each frame. The system learns from training data and automatically generates accurate animations based on high-level parameters, making the system self-sufficient and eliminating the need for manual frame-by-frame posing.
Solution Approach 2:
The neural network is pre-trained on large datasets of realistic motion data before deployment. This preliminary training action enables the system to generate accurate animations quickly during runtime without requiring manual adjustment or iterative refinement, thus improving both accuracy and productivity.
3Adaptability or versatility
If conventional systems generate simple locomotion animations, then basic motions can be created, but complex animation sequences (dance, martial arts) cannot be generated accurately
Solution Approach 1:
The neural network system is designed to handle multiple types of motions uniformly. By training on diverse motion data including locomotion, dance, and martial arts, the system achieves universal applicability across different motion categories while maintaining high accuracy for each specific type through learned motion patterns.
Solution Approach 2:
The patent transitions from generating simple 2D or basic 3D locomotion to full 3D complex animation sequences. The neural network operates in the additional dimension of temporal coherence and spatial realism, enabling accurate generation of complex motions by learning from multi-dimensional training data that captures nuanced human movement.
4Manufacturing precision
If large numbers of frames are generated and processed, then animation detail can be improved, but computing power and time requirements become excessive
Solution Approach 1:
The system extracts only the essential information needed for animation generation from training data, storing it in compressed form within the neural network weights. During runtime, the system generates animations by processing minimal input parameters through the pre-trained network, extracting detailed motion information on-demand without storing or processing large numbers of individual frames.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a digital animation of a digital animation character by utilizing a generative adversarial network and a hip motion prediction network. For example, the disclosed systems can utilize an unconditional generative adversarial network to generate a sequence of local poses of a digital animation character based on an input of a random code vector. The disclosed systems can also utilize a conditional generative adversarial network to generate a sequence of local poses based on an input of a set of keyframes. Based on the sequence of local poses, the disclosed systems can utilize a hip motion prediction network to generate a sequence of global poses based on hip velocities. In addition, the disclosed systems can generate an animation of a digital animation character based on the sequence of global poses.