3D Looping Animation From Still Images With Neural Pose Transitions
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Solution Overview
Problem
Conventional systems face challenges in generating physically plausible 3D looping animations from 2D images, particularly in accurately representing complex body movements and efficiently utilizing computational resources, while existing methods often require large amounts of data and are computationally expensive.
Innovation Solution
The looping 3D animation system integrates an animation transition neural network and an animation rendering neural network to create realistic 3D animations from a single still image, utilizing a 3D body model and neural in-betweening for non-linear motion interpolation, and equivariance models for efficient rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 2D rigging and 2D joint interpolation are used to animate images, then the animation can be created with simple methods, but the physical plausibility of the movement deteriorates
Solution Approach 1:
The patent replaces traditional 2D rigging and joint interpolation methods with a neural network-based system that learns physically plausible motion patterns from data. The neural network models human motion dynamics to generate animations that obey physical laws, substituting mechanical animation techniques with data-driven intelligent systems.
Solution Approach 2:
The patent transforms the animation generation process by changing from fixed 2D joint constraints to continuous 3D pose parameters optimized by neural networks. This allows the system to explore a broader parameter space of physically valid configurations while maintaining ease of use through automated generation.
2Adaptability or versatility
If techniques are developed to create 3D animation sequences from various starting positions, then the flexibility of animation generation is improved, but the difficulty of generating new sequences from user-specified initial poses increases
Solution Approach 1:
The patent implements feedback mechanisms where the neural network receives the user-specified initial pose as input and adjusts the animation sequence to ensure smooth transitions from that specific starting position. The system iteratively refines the animation to satisfy both the desired motion and the constraint of starting from the user-provided pose.
Solution Approach 2:
The patent performs preliminary processing of the user-specified initial pose by the neural network to pre-compute appropriate transition parameters and motion plans. This preliminary action prepares the animation system to seamlessly integrate the user's pose specification into the generated sequence, reducing the difficulty of handling arbitrary starting positions.
3Productivity
If conventional methods are used to generate 3D looping animations, then the process can be completed, but the computational resources and time required increase significantly
Solution Approach 1:
The patent performs preliminary training of neural networks on motion data beforehand, so that during actual animation generation, the pre-learned models can quickly produce results without requiring intensive real-time computation. This shifts computational burden to an offline training phase, improving online generation speed and efficiency.
Solution Approach 2:
The patent uses neural networks to learn and copy underlying motion patterns and physical principles from training data, rather than computing each animation from scratch using resource-intensive physics simulations. This allows rapid generation of plausible animations by leveraging learned representations.
Data Source
AI summary
A system that utilizes neural networks to generate looping animations from still images. The system fits a 3D model to a pose of a person in a digital image. The system receives a 3D animation sequence that transitions between a starting pose and an ending pose. The system generates, utilizing an animation transition neural network, first and second 3D animation transition sequences that respectively transition between the pose of the person and the starting pose and between the ending pose and the pose of the person. The system modifies each of the 3D animation sequence, the first 3D animation transition sequence, and the second 3D animation transition sequence by applying a texture map. The system generates a looping 3D animation by combining the modified 3D animation sequence, the modified first 3D animation transition sequence, and the modified second 3D animation transition sequence.