Last year Adobe launched Firefly, its latest generative AI model building on its previous SenseiAI, and now the company is showing how it'll be used its video editing app, Premiere Pro. In an early sneak, it demonstrated a few key features arriving later this year, including Object Addition & Removal, Generative Extend and Text to Video. The new features will likely be popular, as video cleanup is one a common (and painful) task. The first feature, Generative Extend, addresses a problem editors face on nearly every edit: clips that are too short. "Seamlessly add frames to make clips longer, so it's easier to perfectly time edits and add smooth transitions," Adobe states.

Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores this in persistence diagrams (PDs). As TDA is commonly used in the analysis of high dimensional data sets, a sufficiently large amount of PDs that allow performing statistical analysis is typically unavailable or requires inordinate computational resources. In this paper, we propose random persistence diagram generation (RPDG), a method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned (i) by a parametric model based on pairwise interacting point processes for inference of persistence diagrams and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. The parametric model combines a Dirichlet partition to capture spatial homogeneity of the location of points in a PD and a step function to capture the pairwise interaction between them. The RJ-MCMC algorithm incorporates trans-dimensional addition and removal of points and same-dimensional relocation of points across samples of PDs. The efficacy of RPDG is demonstrated via an example and a detailed comparison with other existing methods is presented.