Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a new passive approach to capture time-varying scene geometry in large acquisition volumes from multi-view video. It can be applied to reconstruct complete moving models of human actors that feature even slightest dynamic geometry detail, such as wrinkles and folds in clothing, and that can be vied from 360deg. Starting from multi-view video streams recorded under calibrated lighting,  first perform marker-less human motion capture based on a smooth template with no high-frequency surface detail. Subsequently, surface reflectance and time-varying normal fields are estimated based on the coarse template shape. The main contribution of this paper is a new statistical approach to solve the non-trivial problem of transforming the captured normal field that is defined over the smooth non-planar 3D template into true 3D displacements. Our spatio-temporal reconstruction method outputs displaced geometry that is accurate at each time step of video and temporally smooth, even if the input data are affected by noise.
['Naveed Ahmed', 'Christian Theobalt', 'Petar Dobrev', 'Hans-Peter Seidel', 'Sebastian Thrun']
I am writing a research paper on a idea Robust fusion of dynamic shape and normal capture for high-quality reconstruction of time-varying geometry
[{"from": "human", "value": "I am writing a research paper on a idea Robust fusion of dynamic shape and normal capture for high-quality reconstruction of time-varying geometry"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a new passive approach to capture time-varying scene geometry in large acquisition volumes from multi-view video. It can be applied to reconstruct complete moving models of human actors that feature even slightest dynamic geometry detail, such as wrinkles and folds in clothing, and that can be vied from 360deg. Starting from multi-view video streams recorded under calibrated lighting,  first perform marker-less human motion capture based on a smooth template with no high-frequency surface detail. Subsequently, surface reflectance and time-varying normal fields are estimated based on the coarse template shape. The main contribution of this paper is a new statistical approach to solve the non-trivial problem of transforming the captured normal field that is defined over the smooth non-planar 3D template into true 3D displacements. Our spatio-temporal reconstruction method outputs displaced geometry that is accurate at each time step of video and temporally smooth, even if the input data are affected by noise."}]