Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lor-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or movemes, from a training set of 2-D projected poses obtained from still images taken at various camera angles. The proposed technique for basis discovery is data-driven rather than hand-designed. The learned representation is rotation invariant, and can reconstruct any training instance from multiple viewing angles.  apply our method to modeling human poses in sports (via the Leeds Sports Dataset), and demonstrate the effectiveness of the learned bases in a range of applications such as activity classification, inference of dynamics from a single frame, and synthetic representation of movements.
['Matteo Ruggero Ronchi', 'Joon Sik Kim', 'Yisong Yue']
I am writing a research paper on a idea A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
[{"from": "human", "value": "I am writing a research paper on a idea A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lor-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or movemes, from a training set of 2-D projected poses obtained from still images taken at various camera angles. The proposed technique for basis discovery is data-driven rather than hand-designed. The learned representation is rotation invariant, and can reconstruct any training instance from multiple viewing angles.  apply our method to modeling human poses in sports (via the Leeds Sports Dataset), and demonstrate the effectiveness of the learned bases in a range of applications such as activity classification, inference of dynamics from a single frame, and synthetic representation of movements."}]