Unsupervised discovery of character dictionaries in animation movies

Automatic content analysis of animation movies can enable an objective understanding of character (actor) representations and their portrayals. It can also help illuminate potential markers of unconscious biases and their impact. However, multimedia analysis of movie content has predominantly focused on live-action features. A dearth of multimedia research in this field is because of the complexity and heterogeneity in the design of animated characters-an extremely challenging problem to be generalized by a single method or model. In this paper, we address the problem of automatically discovering characters in animation movies as a first step toward automatic character labeling in these media. Movie-specific character dictionaries can act as a powerful first step for subsequent content analysis at scale. We propose an unsupervised approach which requires no prior information about the characters in a movie. We first use a deep neural network-based object detector that is trained on natural images to identify a set of initial character candidates. These candidates are further pruned using saliency constraints and visual object tracking. A character dictionary per movie is then generated from exemplars obtained by clustering these candidates. We are able to identify both anthropomorphic and nonanthropomorphic characters in a dataset of 46 animation movies with varying composition and character design. Our results indicate high precision and recall of the automatically detected characters compared to human-annotated ground truth, demonstrating the generalizability of our approach

Video Abstracting at a Semantical Level

One the most common form of a video abstract is the movie trailer. Contemporary movie trailers share a common structure across genres which allows for an automatic generation and also reflects the corresponding moviea s composition. In this thesis a system for the automatic generation of trailers is presented. In addition to action trailers, the system is able to deal with further genres such as Horror and comedy trailers, which were first manually analyzed in order to identify their basic structures. To simplify the modeling of trailers and the abstract generation itself a new video abstracting application was developed. This application is capable of performing all steps of the abstract generation automatically and allows for previews and manual optimizations. Based on this system, new abstracting models for horror and comedy trailers were created and the corresponding trailers have been automatically generated using the new abstracting models. In an evaluation the automatic trailers were compared to the original Trailers and showed a similar structure. However, the automatically generated trailers still do not exhibit the full perfection of the Hollywood originals as they lack intentional storylines across shots

Content-Based Video Description for Automatic Video Genre Categorization

International audienceIn this paper, we propose an audio-visual approach to video genre categorization. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal structural level, we asses action contents with respect to human perception. Further, color perception is quantified with statistics of color distribution, elementary hues, color properties and relationship of color. The last category of descriptors determines statistics of contour geometry. An extensive evaluation of this multi-modal approach based on more than 91 hours of video footage is presented. We obtain average precision and recall ratios within [87% − 100%] and [77% − 100%], respectively,nwhile average correct classification is up to 97%. Additionally, movies displayed according to feature-based coordinates in a virtual 3D browsing environment tend to regroup with respect to genre, which has potential application with real content-based browsing systems

HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Video genre categorization and representation using audio-visual information

International audienceWe propose an audio-visual approach to video genre classification using content descriptors that exploit audio, color, temporal, and contour information. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At the temporal structure level, we consider action content in relation to human perception. Color perception is quantified using statistics of color distribution, elementary hues, color properties, and relationships between colors. Further, we compute statistics of contour geometry and relationships. The main contribution of our work lies in harnessingn the descriptive power of the combination of these descriptors in genre classification. Validation was carried out on over 91 h of video footage encompassing 7 common video genres, yielding average precision and recall ratios of 87% to 100% and 77% to 100%, respectively, and an overall average correct classification of up to 97%. Also, experimental comparison as part of the MediaEval 2011 benchmarkingn campaign demonstrated the efficiency of the proposed audiovisual descriptors over other existing approaches. Finally, we discuss a 3-D video browsing platform that displays movies using efaturebased coordinates and thus regroups them according to genre