Anatomically-Constrained Local Model for Facial Performance Capture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current facial performance capture techniques face challenges in achieving high-quality, detailed face shapes and dynamics while using less-constrained acquisition setups, often resulting in unstable results due to mismatched blendshapes and requiring extensive pre-acquired expressions and manual stabilization.
Innovation Solution
An anatomically-constrained local subspace model is introduced, which segments the face into patches with a local shape subspace and anatomical subspace, using anatomical bone structures to constrain deformation and motion, allowing for high-quality facial performance capture from a single camera view with automatic rigid stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional global blendshape rigs are used for facial performance capture, then detailed face shapes and dynamics can be achieved, but the acquisition setup becomes highly constrained and requires extensive pre-acquired expressions
Solution Approach 1:
The face is segmented into multiple local patches, each with its own blendshape subspace. This segmentation allows the system to capture detailed local facial dynamics without requiring a globally constrained acquisition setup, as each patch can be independently modeled and tracked.
Solution Approach 2:
Different parts of the face are modeled with locally-adapted blendshape subspaces that capture region-specific deformation characteristics. This local quality approach enables high-detail reconstruction of face shapes and dynamics while reducing the need for extensive global constraints and pre-acquired expressions.
2Device complexity
If local blendshape models are used to reduce constraints, then acquisition setup becomes more flexible, but results become unstable due to mismatched blendshapes
Solution Approach 1:
The face is divided into multiple local patches, each with its own blendshape subspace. This segmentation provides the flexibility of local models while maintaining stability through distributed modeling, as each patch independently captures local deformations without global mismatch issues.
Solution Approach 2:
The system uses subject-specific parameters including skin thickness and local deformation characteristics to adapt the blendshape subspaces. These parameter changes ensure that each local patch is properly constrained by anatomical properties, maintaining result stability while allowing acquisition flexibility.
3Measurement precision
If extensive pre-acquired expressions are collected to improve model accuracy, then face shape detail improves, but the time and resources required increase significantly
Solution Approach 1:
By segmenting the face into local patches with region-specific blendshape subspaces, the system achieves detailed face shape capture using fewer global expressions. Each local subspace learns from limited local deformations, reducing the need for extensive comprehensive expression sets.
Solution Approach 2:
The locally-adapted blendshape subspaces capture region-specific deformation patterns efficiently, requiring fewer pre-acquired expressions compared to global models. This local quality approach maintains high measurement precision while significantly reducing pre-acquisition time and resources.
4Reliability
If manual stabilization is applied to correct mismatched blendshapes, then result stability improves, but the process complexity and time required increase
Solution Approach 1:
The segmentation into local patches with independent blendshape subspaces eliminates the need for manual stabilization by preventing global mismatch issues. Each local patch is self-contained and anatomically-constrained, achieving result stability through the modeling structure itself rather than post-processing corrections.
Solution Approach 2:
The anatomically-constrained local model is self-stabilizing through its inherent structure. The skin thickness constraints and local deformation modeling automatically ensure consistency without requiring manual intervention, reducing processing complexity while maintaining reliability.
Data Source
AI summary
Techniques and systems are described for generating an anatomically-constrained local model and for performing performance capture using the model. The local model includes a local shape subspace and an anatomical subspace. In one example, the local shape subspace constrains local deformation of various patches that represent the geometry of a subject's face. In the same example, the anatomical subspace includes an anatomical bone structure, and can be used to constrain movement and deformation of the patches globally on the subject's face. The anatomically-constrained local face model and performance capture technique can be used to track three-dimensional faces or other parts of a subject from motion data in a high-quality manner. Local model parameters that best describe the observed motion of the subject's physical deformations (e.g., facial expressions) under the given constraints are estimated through optimization. The optimization can solve for rigid local patch motion, local patch deformation, and the rigid motion of the anatomical bones. The solution can be formulated as an energy minimization problem for each frame that is obtained for performance capture.