Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and ight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). Based on an extensive corpus of motion capture data, with physical and perceptual ground truth,  analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, hover, prone to over-fitting. To mitigate this  formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data.
['Leonid Sigal', 'David J. Fleet', 'Nikolaus F. Troje', 'Micha Livne']
I am writing a research paper on a idea Human attributes from 3D pose tracking
[{"from": "human", "value": "I am writing a research paper on a idea Human attributes from 3D pose tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and ight) and aspects of mental state (e.g., happiness or sadness). This task is useful for man-machine communication, and it provides a natural benchmark for evaluating the performance of 3D pose tracking methods (vs. conventional Euclidean joint error metrics). Based on an extensive corpus of motion capture data, with physical and perceptual ground truth,  analyze the inference of subtle biologically-inspired attributes from cyclic gait data. It is shown that inference is also possible with partial observations of the body, and with motions as short as a single gait cycle. Learning models from small amounts of noisy video pose data is, hover, prone to over-fitting. To mitigate this  formulate learning in terms of domain adaptation, for which mocap data is uses to regularize models for inference from video-based data."}]