Abstract-With the idea of social network analysis, we propose a novel way to analyze movie videos from the perspective of social relationships rather than audiovisual features. To appropriately describe role's relationships in movies, we devise a method to quantify relations and construct role's social networks, called RoleNet. Based on RoleNet, we are able to perform semantic analysis that goes beyond conventional feature-based approaches. In this work, social relations between roles are used to be the context information of video scenes, and leading roles and the corresponding communities can be automatically determined. The results of community identification provide new alternatives in media management and browsing. Moreover, by describing video scenes with role's context, social-relation-based story segmentation method is developed to pave a new way for this widely-studied topic. Experimental results show the effectiveness of leading role determination and community identification. We also demonstrate that the social-based story segmentation approach works much better than the conventional tempo-based method. Finally, we give extensive discussions and state that the proposed ideas provide insights into context-based video analysis.

Figure 1: Given a single photo as input (far left), we create a 3D animatable version of the subject, which can now walk towards the viewer (middle). The 3D result can be experienced in augmented reality (right); in the result above the user has virtually hung the artwork with a HoloLens headset and can watch the character run out of the painting from different views. Please see all results in the supplementary video: https://youtu.be/G63goXc5MyU. AbstractWe present a method and application for animating a human subject from a single photo. E.g., the character can walk out, run, sit, or jump in 3D. The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3D, 2) a novel 2D warping method to deform a posable template body model to fit the person's complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions. We compare to state-of-the-art related methods and evaluate results with human studies. Further, we present an interactive interface that allows re-posing the person in 3D, and an augmented reality setup where the animated 3D person can emerge from the photo into the real world. We demonstrate the method on photos, posters, and art. The project page is at https

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Roles in a movie form a small society and their interrelationship provides clues for movie understanding. Based on this observation, we present a new viewpoint to perform semantic movie analysis. Through checking the co-occurrence of roles in different scenes, we construct a roles' social network to describe their relationships. We introduce the concept of social network analysis to elaborately identify leading roles and the hidden communities. With the results of community identification, we perform storyline detection that facilitates more flexible movie browsing and higher-level movie analysis. The experimental results show that the proposed community identification method is accurate and is robust to errors.