While digital humans are key aspects of the rapidly evolving areas of virtual reality, gaming, and online communications, many applications would benefit from using digital personalized (stylized) representations of users, as they were shown to highly increase immersion, presence and emotional response. In particular, depending on the target application, one may want to look like a dwarf or an elf in a heroic fantasy world, or like an alien on another planet, in accordance with the style of the narrative. While creating such virtual replicas requires stylization of the user’s features onto the virtual character, no formal study has however been conducted to assess the ability to recognize stylized characters. In this paper, we present a perceptual study investigating the effect of the degree of stylization on the ability to recognize an actor, and the subjective acceptability of stylizations. Results show that recognition rates decrease when the degree of stylization increases, while acceptability of the stylization increases. These results provide recommendations to achieve good compromises between stylization and recognition, and pave the way to new stylization methods providing a tradeoff between stylization and recognition of the actor.

In this paper, we present a fully automatic pipeline for generating and stylizing high geometric and textural quality facial rigs. They are automatically rigged with facial blendshapes for animation, and can be used across platforms for applications including virtual reality, augmented reality, remote collaboration, gaming and more. From a set of input facial photos, our approach is to be able to create a photorealistic, fully rigged character in less than seven minutes. The facial mesh reconstruction is based on state-of-the art photogrammetry approaches. Automatic landmarking coupled with ICP registration with regularization provide direct correspondence and registration from a given generic mesh to the acquired facial mesh. Then, using deformation transfer, existing blendshapes are transferred from the generic to the reconstructed facial mesh. The reconstructed face is then fit to the full body generic mesh. Extra geometry such as jaws, teeth and nostrils are retargeted and transferred to the character. An automatic iris color extraction algorithm is performed to colorize a separate eye texture, animated with dynamic UVs. Finally, an extra step applies a style to the photorealis-tic face to enable blending of personalized facial features into any other character. The user's face can then be adapted to any human or non-human generic mesh. A pilot user study was performed to evaluate the utility of our approach. Up to 65% of the participants were successfully able to discern the presence of one's unique facial features when the style was not too far from a humanoid shape.

This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

Investigating cell dynamics during early zebrafish embryogenesis requires specific image acquisition and analysis strategies. Multiharmonic microscopy, i.e., second- and third-harmonic generations, allows imaging cell divisions and cell membranes in unstained zebrafish embryos from 1- to 1000-cell stage. This paper presents the design and implementation of a dedicated image processing pipeline (tracking and segmentation) for the reconstruction of cell dynamics during these developmental stages. This methodology allows the reconstruction of the cell lineage tree including division timings, spatial coordinates, and cell shape until the 1000-cell stage with minute temporal accuracy and micrometer spatial resolution. Data analysis of the digital embryos provides an extensive quantitative description of early zebrafish embryogenesis.