Character Animation Neural Network for Temporally Coherent Video Synthesis
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Solution Overview
Problem
Conventional video synthesis systems face inaccuracies and inflexibilities in generating digital video animations, particularly for in-the-wild animation sequences and characters, struggling with loose garments and complex textures, and require extensive training data and specific templates, limiting their applicability and efficiency.
Innovation Solution
A character animation neural network with dual network branches is used to generate digital videos by representing motion and pose features, refining pose features, and demodulating neural network weights to capture dynamic appearance changes, allowing for high-quality generation of videos with loose garments and complex motions without the need for extensive training data or specific templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video synthesis systems are used to generate digital video animations, then basic animation generation is achieved, but animation accuracy and system flexibility deteriorate for in-the-wild sequences and characters
Solution Approach 1:
The system employs a neural network with dynamic weight adjustment capabilities, where weights are modulated based on motion embeddings derived from input poses. This allows the network to adapt its parameters in real-time according to the specific motion characteristics, achieving both high accuracy for specific sequences and flexibility for diverse applications without requiring extensive retraining
Solution Approach 2:
The invention changes the parameters of the neural network dynamically by using motion embeddings to modulate weights. Instead of fixed weights trained on extensive data, the system adjusts weights based on the specific motion sequence being processed, enabling accurate generation for in-the-wild sequences while maintaining system flexibility
2Manufacturing precision
If conventional video synthesis systems process loose garments and complex textures, then basic animation generation is achieved, but manufacturing precision and detail accuracy deteriorate
Solution Approach 1:
The system performs preliminary extraction of motion embeddings from the input poses before generating the final animation. This preliminary action captures the essential motion characteristics that affect garment and texture appearance, allowing the network to focus computational resources on accurately rendering these details rather than processing all aspects of the animation equally
Solution Approach 2:
The motion embedding serves as an intermediary that bridges the input poses and the neural network processing. It encodes motion-specific information that directly influences weight modulation, enabling the system to accurately handle complex garments and textures by providing targeted information about their motion characteristics
3Productivity
If conventional video synthesis systems are trained with extensive training data and specific templates, then basic animation generation is achieved, but productivity and efficiency deteriorate
Solution Approach 1:
The neural network is designed with universal applicability through its weight modulation mechanism. A single trained network can handle diverse motion sequences and character types by dynamically adjusting its weights based on motion embeddings, eliminating the need for extensive specific training data for each application while maintaining high generation efficiency
Solution Approach 2:
The system performs self-adaptation by automatically modulating its own weights based on the input motion characteristics. The motion embeddings enable the network to serve itself by adjusting its parameters according to the specific task at hand, reducing dependency on externally provided training data and templates
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and method that utilize a character animation neural network informed by motion and pose signatures to generate a digital video through person-specific appearance modeling and motion retargeting. In particular embodiments, the disclosed systems implement a character animation neural network that includes a pose embedding model to encode a pose signature into spatial pose features. The character animation neural network further includes a motion embedding model to encode a motion signature into motion features. In some embodiments, the disclosed systems utilize the motion features to refine per-frame pose features and improve temporal coherency. In certain implementations, the disclosed systems also utilize the motion features to demodulate neural network weights used to generate an image frame of a character in motion based on the refined pose features.