Hand Tracking via Skinned Tetrahedral Mesh Deformation
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Solution Overview
Problem
Conventional hand tracking systems in virtual and augmented reality face accuracy and latency issues, leading to an unsatisfying user experience due to difficulties in accurately capturing and processing hand gestures.
Innovation Solution
A hand tracking system that uses a skinned tetrahedral mesh to volumetrically deform a signed distance field, allowing for efficient estimation of hand poses by interpolating precomputed signed distance values and minimizing an energy function based on depth image data, thereby avoiding explicit generation of the articulated signed distance function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hand tracking systems optimize energy functions to find minimum corresponding to correct hand pose, then hand pose estimation is achieved, but accuracy and latency issues occur resulting in unsatisfying user experience
Solution Approach 1:
The system precomputes signed distance fields for multiple predefined hand poses before runtime. During actual hand tracking, the system only needs to interpolate between these precomputed fields and identify the minimum energy pose, dramatically reducing computation time and latency while maintaining accuracy.
Solution Approach 2:
Instead of computing signed distance fields from scratch for each frame, the system creates copies of precomputed signed distance fields corresponding to different hand poses and uses interpolation to generate the actual field. This copying approach avoids redundant computations and speeds up real-time tracking.
2Measurement precision
If the system explicitly generates articulated signed distance function for each hand pose, then accurate hand tracking is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The system precomputes and stores signed distance fields for a set of representative hand poses before runtime. During actual tracking, it interpolates between these precomputed fields based on the current hand configuration, avoiding the need to explicitly generate articulated signed distance functions for every possible pose in real-time.
Solution Approach 2:
The system creates interpolated copies of precomputed signed distance fields rather than explicitly generating new articulated fields. By copying and blending precomputed fields corresponding to nearby poses, the system achieves accurate representation of the current hand pose without the computational burden of explicit generation.
3Productivity
If the system processes hand gestures in real-time with high frame rates, then user experience is enhanced, but processing speed requirements increase making accurate tracking difficult
Solution Approach 1:
By precomputing signed distance fields for multiple hand poses before runtime, the system eliminates the need for complex real-time computations. During high-frame-rate operation, the system only performs lightweight interpolation and minimum-finding operations on precomputed data, maintaining both high speed and accuracy.
Solution Approach 2:
The system uses precomputed field copies and interpolation to rapidly generate accurate signed distance representations for each frame. This approach allows the system to meet high frame rate requirements while maintaining precision, as the expensive computation is performed offline rather than in real-time.
Data Source
AI summary
An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.