In this work, we present a 2-stage pipeline for object-agnostic Hand-Object Reconstruction. First, we robustly retrieve viewpoints relying on a learned pairwise camera pose estimator trainable with a low data regime, followed by a globalized Shonan pose averaging. Second, we simultaneously estimate detailed 3D hand-object shapes and refine camera poses using a differential renderer-based optimizer.

In this work, we introduce a high quality hand-object dataset with 3D pose, shape, texture annotations and parametric models. We also devise a pipeline for category-agnostic 3D hand-object reconstruction baselines.

4DHumanOutfit is a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain different actors wearing different outfits each while and performing different motions in each outfit. In this way, the dataset can be seen as a cube of data containing 4D motion sequences along the axes identity, outfit, and motion.

We employ machine learning (ML) and deep learning (DL) methodologies to forecast the behaviors of electric vehicle (EV) users. By analyzing and contrasting these outcomes, we derive insights to comprehend variations in performance efficacy.

we discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example.