You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Our framework can easily be extended to human pose estimation. We model a keypoint’s location as a one-hot mask, and adopt Mask R-CNN to predict K masks, one for each of K keypoint types (e.g., left shoulder, right elbow). This task helps demonstrate the flexibility of Mask R-CNN. Finally, we showcase the generality of our framework via the task of human pose estimation on the COCO keypoint dataset [28]. By viewing each keypoint as a one-hot binary mask, with minimal modification Mask R-CNN can be applied to detect instance-specific poses. Mask R-CNN surpasses the winner of the 2016 COCO keypoint competition, and at the same time runs at 5 fps. Mask R-CNN, therefore, can be seen more broadly as a flexible framework for instance-level recognition and can be readily extended to more complex tasks.

Question: Can we use Faster R-CNN for human pose estimation ?
Yes we can use Faster R-CNN for human pose estimation.