Detecting and quantifying isoforms from RNA-seq data is an important and challenging task. The problem is often ill-posed since different combinations of isoforms may correctly explain the observed read counts, particularly at low coverage. Assuming that some isoforms are shared between samples, simultaneously detecting isoforms from multiple samples can yield better estimation by increasing the total number of reads available and the diversity in relative abundances between different transcripts. We propose a new method for solving this isoform deconvolution problem jointly across several samples. The method is an extension of the FlipFlop technique, which was initially proposed to identify and quantify isoforms from a single sample, and is formulated as a convex optimization problem. We demonstrate the benefits of combining several samples for isoform detection, and show that our approach outperforms simple pooling strategies and other methods based on mixed integer programming. Source code is freely available as an R package from the Bioconductor web site (http://www.bioconductor.org/) and more information is available at http://cbio.ensmp.fr/flipflop.