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Project Description.nb
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(* Content-type: application/vnd.wolfram.mathematica *)
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Human Body Pose Estimation is a very challenging problem that has widespread \
applications. Human Computer Interaction, Robotics, Filming Industry and \
Medical applications are few such venues where pose and shape estimation has \
proven to be very helpful. Although the field is in a constant cycle of \
achieving better results, little work has been done to incorporate the state \
of art models in the wolfram language for community to use and benefit from. \
This project aims to implement 3D Human pose estimation from image and video \
data using neural networks using Mathematica.\
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The objective of this project is to potentially leverage existing \
advancements in the field, such as AlphaPose and MotionBERT, and adapt them \
to the Mathematica platform as well as using existing wolfram functionalities \
such as CenterNet and Depth Perception Net to estimate Human Pose in a \
3-dimensional format. The initial focus will be on analyzing the existing \
models and understanding their underlying methodologies. By studying multiple \
approaches, this project aims to implement one of these models using Wolfram \
Language, providing an easy to use implementation within this environment.\
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The initial scope of this project limits to estimating the 3D pose of a \
person from video. This will be obtained in several key steps. Firstly, the \
project key focus will be processing input images and extracting the 3D \
skeletons or heat-maps. This will be achieved by accurately localizing the \
key joints and estimating their positions and orientations. The next step for \
potential development will involve mesh regression techniques to extract the \
3D body shape from the input image enabling the estimation of the full 3D \
representation of the human body. Expanding beyond single images, I will then \
explore techniques to extend the pose and shape estimation to video \
sequences. This will involve incorporating temporal information and \
leveraging the sequential nature of video frames to improve the accuracy and \
robustness of the estimations.\
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As a part of the future work, the project will aim to tackle the challenge of \
multi-person scenarios with occlusions. Addressing this problem will involve \
developing techniques to handle occluded body parts and accurately estimate \
the poses and shapes of multiple individuals within the same frame.\
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