Abstract We present a novel temporally coherent stylized rendering technique working entirely at the compositing stage. We first generate a distribution of 3D anchor points using an implicit grid based on the local object positions stored in a G‐buffer, hence following object motion. We then draw splats in screen space anchored to these points so as to be motion coherent. To increase the perceived flatness of the style, we adjust the anchor points density using a fractalization mechanism. Sudden changes are prevented by controlling the anchor points opacity and introducing a new order‐independent blending function. We demonstrate the versatility of our method by showing a large variety of styles thanks to the freedom offered by the splats content and their attributes that can be controlled by any G‐buffer.

We present a method for easily drafting expressive character animation by playing with instrumented rigid objects. We parse the input 6D trajectories (position and orientation over time) – called spatial motion doodles – into sequences of actions and convert them into detailed character animations using a dataset of parameterized motion clips which are automatically fitted to the doodles in terms of global trajectory and timing. Moreover, we capture the expressiveness of user-manipulation by analyzing Laban effort qualities in the input spatial motion doodles and transferring them to the synthetic motions we generate. We validate the ease of use of our system and the expressiveness of the resulting animations through a series of user studies, showing the interest of our approach for interactive digital storytelling applications dedicated to children and non-expert users, as well as for providing fast drafting tools for animators.

Pretend play is a storytelling technique, naturally used from very young ages, which relies on object substitution to represent the characters of the imagined story. We propose a system which assists the storyteller by generating a virtualized story from a recorded dialogue performed with 3D printed figurines. We capture the gestures and facial expressions of the storyteller using Kinect cameras and IMU sensors and transfer them to their virtual counterparts in the story-world. As a proof-of-concept, we demonstrate our system with an improvised story involving a prince and a witch, which was successfully recorded and transferred into 3D animation.

MOTIVATION: High-throughput gene expression profiling yields genomic signatures that allow the prediction of clinical conditions including patient outcome. However, these signatures have limitations, such as dependency on the training set, and worse, lack of generalization. RESULTS: We propose a novel algorithm called ITI (interactome-transcriptome integration), to extract a genomic signature predicting distant metastasis in breast cancer by superimposition of large-scale protein-protein interaction data over a compendium of several gene expression datasets. Training on two different compendia showed that the estrogen receptor-specific signatures obtained are more stable (11-35% stability), can be generalized on independent data and performs better than previously published methods (53-74% accuracy). AVAILABILITY: The ITI algorithm source code from analysis are available under CeCILL from the ITI companion website: http://bioinformatique.marseille.inserm.fr/iti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.