Neural Network Animation Generation via Sliding Window Phase Prediction
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Solution Overview
Problem
Current systems for generating realistic character animations in electronic games require substantial time and effort from developers to create detailed and lifelike movements, often relying on manual adjustments of character models and limited motion capture data, resulting in unrealistic and noisy animations.
Innovation Solution
A dynamic animation generation system using machine learning techniques, specifically training neural networks on motion capture video to predict and generate smooth, realistic character animations by processing sliding windows of frames and local motion phases, allowing for automated analysis and improved animation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-tuned techniques are used to adjust skeleton objects for realistic movement, then movement realism is improved, but development time and complexity increase substantially
Solution Approach 1:
The system enables automatic animation generation where the computer system performs the adjustment of skeleton objects and character model movement autonomously based on motion capture video, eliminating the need for manual hand-tuning by developers while maintaining realistic movement through neural network-based prediction
Solution Approach 2:
The patent replaces the manual mechanical adjustment process with an automated machine learning system that uses neural networks to predict character pose and generate animation frames, substituting human developer effort with automated computational processing
2Reliability
If more detailed character models and movements are created, then game realism is improved, but developer burden increases
Solution Approach 1:
The system automatically generates detailed character animations by processing motion capture video through trained neural networks, allowing the system to self-generate complex movement patterns without requiring developers to manually create each animation detail
Solution Approach 2:
The system creates realistic character movements by copying and adapting motion patterns from motion capture video of real actors, using the neural network to replicate human movement nuances in the virtual character model without requiring developers to design each movement from scratch
3Ease of operation
If traditional animation systems are used, then development process is simple, but animation smoothness and quality deteriorate
Solution Approach 1:
The system ensures continuous and smooth animation by generating each frame based on temporal context from previous and future frames using a sliding window approach, maintaining continuous motion flow and preventing discontinuities that plague traditional frame-by-frame animation systems
Solution Approach 2:
The system uses future frames in the sliding window as feedback to guide the prediction of current frames, allowing the animation system to anticipate and correct potential discontinuities before they occur, thereby improving overall animation smoothness and temporal consistency
4Reliability
If motion capture data is used directly, then realistic movement is captured, but noisy and incomplete pose information reduces animation quality
Solution Approach 1:
The system performs preliminary processing of motion capture data by training neural networks on the raw data beforehand, enabling the model to learn and compensate for noise and missing information in advance, so that the final animation generation produces clean, complete pose information even when input data is noisy or incomplete
Solution Approach 2:
The sliding window mechanism provides temporal feedback by incorporating information from multiple surrounding frames, allowing the system to reconstruct missing pose information and filter noise through contextual analysis of the motion sequence, improving animation quality while preserving movement authenticity
Data Source
AI summary
Embodiments of the systems and methods described herein provide a dynamic animation generation system that can apply a real-life video clip with a character in motion to a first neural network to receive rough motion data, such as pose information, for each of the frames of the video clip, and overlay the pose information on top of the video clip to generate a modified video clip. The system can identify a sliding window that includes a current frame, past frames, and future frames of the modified video clip, and apply the modified video clip to a second neural network to predict a next frame. The dynamic animation generation system can then move the sliding window to the next frame while including the predicted next frame, and apply the new sliding window to the second neural network to predict the following frame to the next frame.