Automated Character Rigging for Varied 3D Shapes and Proportions
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Solution Overview
Problem
Existing techniques for creating character animations are labor-intensive, limit artistic creativity due to predefined attributes and templates, and cannot generate animations for characters with varied shapes and proportions.
Innovation Solution
A computer-implemented method that generates animations by processing user input to create textured geometry and weight maps, allowing for the automatic generation of characters with any shapes and proportions, using machine learning models to control artistic style and pose, and automating the rigging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If predefined attributes and clothing are used to customize a generic character, then the character creation process is simplified and automated, but the artistic creativity and variety of character designs are limited
Solution Approach 1:
The system generates images of characters using machine learning models that can create unlimited variations of character designs. Instead of being constrained to predefined templates, the system copies and adapts visual styles from training data to produce novel character appearances while maintaining animatability through automated rigging processes.
Solution Approach 2:
The system allows dynamic adjustment of character parameters such as pose, style, and physical attributes through image generation models. By changing input parameters to the machine learning model, users can generate diverse character designs without being limited to fixed templates, while the automated pipeline maintains consistency in the rigging process.
2Ease of manufacture
If fixed templates define character layout and proportions, then the animation process becomes standardized and easier to automate, but characters with varied shapes and proportions cannot be created
Solution Approach 1:
The system transitions from static fixed templates to dynamic generated images. Character layouts and proportions are no longer fixed but can be dynamically adjusted through image generation parameters. The automated rigging process adapts to each generated character's unique geometry, maintaining ease of animation while enabling diverse character shapes and proportions.
Solution Approach 2:
The system creates a universal character generation pipeline that can handle any character shape or proportion. Instead of requiring different templates for different character types, a single automated system generates images and applies rigging universally across all character variations, enabling both standardization and flexibility.
3Manufacturing precision
If manual processes are used to create character animations, then high quality and control are achieved, but the process is labor intensive and time consuming
Solution Approach 1:
The system implements self-service automation where the character generation and rigging processes perform themselves without manual intervention. The machine learning models automatically generate character images from text or image inputs, and the rigging system automatically creates the necessary animation structures. This maintains quality through algorithmic consistency while dramatically increasing productivity by eliminating manual labor.
Solution Approach 2:
The system replaces manual mechanical animation processes with automated machine learning-based image generation and algorithmic rigging. Instead of animators manually creating and rigging each character, the mechanical process is substituted with automated computational processes that generate characters and their animation structures rapidly while maintaining consistent quality standards.
4Extent of automation
If standardized skeleton joints are used for animation, then the animation process is simplified and automated, but props and characters that do not fit standardized silhouettes cannot be animated
Solution Approach 1:
The system applies local quality by generating rigging and skeleton structures that are specific to each character's unique geometry rather than using universal standardized joints. The automated rigging process analyzes each generated character's local features and creates appropriate animation structures tailored to that character's shape, enabling both automation and flexibility for diverse character types including non-humanoid props.
Data Source
AI summary
One embodiment of a method for generating animations includes generating one or more images of an object based on user input, generating textured geometry based on the one or more images, generating a weight map based on at least one image included in the one or more images, and generating an animation of the object based on the textured geometry, the weight map, and a skeleton.