Computer Vision

A gesture recogniser that drives an interface is judged by whether it eventually fires. One that drives a visual effect inside a recording is judged by when it fires, because the viewer sees the gesture and its consequence in the same footage and will attribute one to the other only if they coincide. For the rotation of an open hand about its long axis, the instant can be obtained directly and exactly from projective geometry, with no classifier, no training data and no calibration. This is the full write-up of the paper.

Special thanks to Sarvesh Talele for his meaningful contributions, support, and wisdom that helped shape this work. Traffic cameras record the circumstances of a great many collisions, but methods that read those recordings automatically are usually trained on annotated footage from the same camera. This work asks what can be recovered without any labelled real-world data at all, using three independent modules and only pre-trained weights.

Text2Video-Zero, from Picsart AI Research, turns a text-to-image diffusion model into a zero-shot video generator with no video training data at all. This write-up explains the method, the motion dynamics and cross-frame attention that keep a generated sequence coherent, and reports what the model produced when run, including where the results break down.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. Deep learning’s breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.

Special thanks to Mega Satish for her meaningful contributions, support, and wisdom that helped shape this work. This paper aims to demonstrate the efficiency of the Adversarial Open Domain Adaption framework for sketch-to-photo synthesis. The unsupervised open domain adaption for generating realistic photos from a hand-drawn sketch is challenging as there is no such sketch of that class for training data.

Special thanks to Mega Satish and Hasan Rizvi for their meaningful contributions, support, and wisdom that helped shape this work. In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos.