**ZoomNet** is a 2D human whole-body pose estimation technique. It aims to localize dense landmarks on the entire human body including face, hands, body, and feet. ZoomNet follows the top-down paradigm. Given a human bounding box of each person, ZoomNet first localizes the easy-to-detect body keypoints and estimates the rough position of hands and face. Then it zooms in to focus on the hand/face areas and predicts keypoints using features with higher resolution for accurate localization. Unlike previous approaches which usually assemble multiple networks, ZoomNet has a single network that is end-to-end trainable. It unifies five network heads including the human body pose estimator, hand and face detectors, and hand and face pose estimators into a single network with shared low-level features.

**DROID-SLAM** is a deep learning based SLAM system. It consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. This layer leverages geometric constraints, improves accuracy and robustness, and enables a monocular system to handle stereo or RGB-D input without retraining. It builds a dense 3D map of the environment while simultaneously localizing the camera within the map.

DROID-SLAM

DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras

ZoomNet

Whole-Body Human Pose Estimation in the Wild

**Convolution-enhanced image Transformer** (**CeiT**) combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantages of Transformers in establishing long-range dependencies. Three modifications are made to the original Transformer: 1) instead of the straightforward tokenization from raw input images, we design an **Image-to-Tokens** (**I2T**) module that extracts patches from generated low-level features; 2) the feed-froward network in each encoder block is replaced with a **Locally-enhanced Feed-Forward** (**LeFF**) layer that promotes the correlation among neighbouring tokens in the spatial dimension; 3) a **Layer-wise Class token Attention** (**LCA**) is attached at the top of the Transformer that utilizes the multi-level representations.

Source	Whole-Body Human Pose Estimation in the Wild
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com