3D Mesh Deformation via Disentangled Meta-Handles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional mesh deformation methods in 3D computer graphics often produce implausible deformations and are difficult to use, as they tend to destroy structural properties like symmetry, and require high computing resources for generating new meshes.
Innovation Solution
A 3D conditional generative model that learns disentangled meta-handles from existing meshes, allowing for intuitive, controllable, and plausible deformations by factorizing deformation space with a small number of meta-handles, using regularizations like fitting, geometric, adversarial, and disentanglement regularization, and employing biharmonic coordinates for smooth propagation of control points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If new polygonal meshes are generated from scratch, then manufacturing precision and structural properties are improved, but computing resources and time consumption increase significantly
Solution Approach 1:
The system pre-learns a set of disentangled meta-handles and their coefficient ranges from a collection of 3D meshes during an offline training phase. This preliminary action stores deformation knowledge in advance, so that during actual deformation operations, the system can quickly apply pre-learned meta-handles without intensive computing, thus resolving the contradiction between mesh quality and computing efficiency
Solution Approach 2:
Instead of generating new meshes from scratch, the system copies and deforms existing meshes using learned meta-handles. The meta-handles represent typical deformation patterns learned from multiple examples, allowing the system to replicate plausible deformations without重新 computing mesh generation, thereby reducing computing resource consumption while maintaining structural properties
2Productivity
If conventional mesh deformation methods are used, then computing resources are reduced, but deformation plausibility and structural property preservation deteriorate
Solution Approach 1:
The system performs self-learning by automatically analyzing a collection of 3D meshes and their deformation handles to discover disentangled meta-handles and their coefficient ranges without human intervention. This self-service capability enables the system to autonomously acquire deformation knowledge, improving deformation plausibility while maintaining computational efficiency during actual operations
Solution Approach 2:
The system employs multiple regularization techniques (fitting regularization, geometric regularization, adversarial regularization, and disentanglement regularization) that provide feedback during the training process. These regularizations guide the learning of meta-handles to ensure they produce plausible deformations that preserve structural properties, thereby improving reliability without sacrificing computing efficiency
3Measurement precision
If many deformation handles are used to control mesh deformation, then deformation control precision is improved, but ease of operation and intuitiveness deteriorate
Solution Approach 1:
The system merges multiple deformation handles into a small set of disentangled meta-handles, where each meta-handle represents a combination of original deformation handles corresponding to a specific deformation axis or semantic component. This merging reduces the number of controls users need to manipulate while maintaining precise deformation control through the learned coefficient ranges of each meta-handle
Solution Approach 2:
The system segments the deformation space into disentangled components, with each meta-handle corresponding to a specific deformation axis or semantic part of the mesh. This segmentation allows users to control deformation intuitively by adjusting coefficients of individual meta-handles, each affecting a specific aspect of the mesh, thereby improving ease of operation while maintaining control precision
Data Source
AI summary
This disclosure includes technologies for deformation of 3D shapes using meta-handles. The disclosed 3D conditional generative system takes control points with biharmonic coordinates as deformation handles for a shape to train a network to learn a set of meta-handles for the shape. Further, each deformation axis of the latent space of deformation is explicitly associated with a meta-handle from a set of disentangled meta-handles, and the disentangled meta-handles factorize plausible deformations of the shape. Advantageously, an intuitive deformation of the shape may be generated by manipulating coefficients of the meta-handles, e.g., via a user interface.