Sparse Neural Generative Inference Based Pose Estimation
Advanced Computer Vision - Fall 2020 - Professor David Fouhey
We envisioned a light weight pose estimation model that learns end-to-end to leverage moving particles that sample portions of the image to inform object pose estimation.
![](/assets/img/SSPF.png)
The image shows the produced pipeline. Movitated by reducing model and time coplexity, we aimed to utilize particle-filter like sampling to attend to areas where objects might be present in high resolution images.