Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely,  specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships beten them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model.  use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical por of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very ll on the Buffy dataset without any training.
['Xianjie Chen', 'Alan L. Yuille']
I am writing a research paper on a idea Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
[{"from": "human", "value": "I am writing a research paper on a idea Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely,  specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships beten them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model.  use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical por of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very ll on the Buffy dataset without any training."}]