Towards Semantic Fast-Forward and Stabilized Egocentric Videos

The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.Comment: Accepted for publication and presented in the First International Workshop on Egocentric Perception, Interaction and Computing at European Conference on Computer Vision (EPIC@ECCV) 201

Real-time shot detection based on motion analysis and multiple low-level techniques

To index, search, browse and retrieve relevant material, indexes describing the video content are required. Here, a new and fast strategy which allows detecting abrupt and gradual transitions is proposed. A pixel-based analysis is applied to detect abrupt transitions and, in parallel, an edge-based analysis is used to detect gradual transitions. Both analysis are reinforced with a motion analysis in a second step, which significantly simplifies the threshold selection problem while preserving the computational requirements. The main advantage of the proposed system is its ability to work in real time and the experimental results show high recall and precision values

Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

Depth map compression via 3D region-based representation

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.Postprint (author's final draft

Video based Animation Synthesis with the Essential Graph

International audienceWe propose a method to generate animations using video-based mesh sequences of elementary movements of a shape. New motions that satisfy high-level user-specified constraints are built by recombining and interpolating the frames in the observed mesh sequences. The interest of video based meshes is to provide real full shape information and to enable therefore realistic shape animations. A resulting issue lies, however, in the difficulty to combine and interpolate human poses without a parametric pose model, as with skeleton based animations. To address this issue, our method brings two innovations that contribute at different levels: Locally between two motion sequences, we introduce a new approach to generate realistic transitions using dynamic time warping; More globally, over a set of motion sequences, we propose the essential graph as an efficient structure to encode the most realistic transitions between all pairs of input shape poses. Graph search in the essential graph allows then to generate realistic motions that are optimal with respect to various user-defined constraints. We present both quantitative and qualitative results on various 3D video datasets. They show that our approach compares favourably with previous strategies in this field that use the motion graph