Automatic generation of 3D animations from text and images

The understanding of information in a text description can be improved by visually accompanying it with images or videos. This opportunity is particularly relevant for books and other traditional instructional material. Videos or, more in general, (interactive) graphics contents, can help to increase the effectiveness of this material, by providing, e.g., an animated representation of the steps to be performed to carry out a given procedure. The generation of 3D animated contents, however, is still very labor-intensive and time-consuming. Systems able to speed up this process offering flexible and easy-to-use interfaces are becoming of paramount importance. Hence, this paper describes a system designed to automatically generate a computer graphics video by processing a text description and a set of associated images. The system combines Natural Language Processing and image analysis for extract- ing information needed to visually represent the procedure depicted in an instruction manual using 3D animations. It relies on a database of 3D models and preconfigured animations that are activated according to the information extracted from the said input. Moreover, by analyzing the images, the system can also generate new animations from scratch. Promising results have been obtained assessing the system performance in a specific use case focused on printers maintenance

Detecting drift in deep learning: A methodology primer

Most machine learning models are trained on historical data to learn a static mapping between their input and output variables. However, they are deployed on continuously streamed data, whose nature is likely to change over time (data or concept drift). As a consequence, model performance may suddenly and substantially degrade, forcing practitioners to continuously update the models to reflect the new data distribution. Few methods, however, are available to reliably detect data drift on heterogeneous data types (structured and unstructured), possibly without requiring labeled data at inference time. In this paper, we review existing methods for dataset drift detection, discuss their applicability to deep neural networks, and experiment on a practical case study related to semi-structured document analysis