# HOnnotate: A method for 3D Annotation of Hand and Object Poses

## TL;DR

HOnnotate introduces a novel joint optimization method for annotating 3D hand and object poses in images, enabling automatic annotation despite occlusions, and creates the first large-scale markerless dataset for this task.

## Contribution

The paper presents a new annotation technique and a comprehensive dataset for 3D hand-object pose estimation, addressing occlusion challenges and enabling training of RGB-based prediction models.

## Key findings

- Created HO-3D, a large-scale 3D annotated dataset with 77,558 frames.
- Developed a single RGB image-based method for hand pose prediction during object interaction.
- Method generalizes to unseen objects despite severe occlusions.

## Abstract

We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method. Our motivation is the current lack of annotated real images for this problem, as estimating the 3D poses is challenging, mostly because of the mutual occlusions between the hand and the object. To tackle this challenge, we capture sequences with one or several RGB-D cameras and jointly optimize the 3D hand and object poses over all the frames simultaneously. This method allows us to automatically annotate each frame with accurate estimates of the poses, despite large mutual occlusions. With this method, we created HO-3D, the first markerless dataset of color images with 3D annotations for both the hand and object. This dataset is currently made of 77,558 frames, 68 sequences, 10 persons, and 10 objects. Using our dataset, we develop a single RGB image-based method to predict the hand pose when interacting with objects under severe occlusions and show it generalizes to objects not seen in the dataset.