Today I read a paper titled “Interactive Character Posing by Sparse Coding”
The abstract is:
Character posing is of interest in computer animation
It is difficult due to its dependence on inverse kinematics (IK) techniques and articulate property of human characters
To solve the IK problem, classical methods that rely on numerical solutions often suffer from the under-determination problem and can not guarantee naturalness
Existing data-driven methods address this problem by learning from motion capture data
When facing a large variety of poses however, these methods may not be able to capture the pose styles or be applicable in real-time environment
Inspired from the low-rank motion de-noising and completion model in lai2011motion, we propose a novel model for character posing based on sparse coding
Unlike conventional approaches, our model directly captures the pose styles in Euclidean space to provide intuitive training error measurements and facilitate pose synthesis
A pose dictionary is learned in training stage and based on it natural poses are synthesized to satisfy users’ constraints
We compare our model with existing models for tasks of pose de-noising and completion
Experiments show our model obtains lower de-noising and completion error
We also provide User Interface(UI) examples illustrating that our model is effective for interactive character posing