Character animation reconstruction from content based motion retrieval

We present the initial design of a motion reconstruction framework for character animation which encompasses the use of supervised and unsupervised learning techniques for the retrieval and synthesis of new realistic motion. Taking advantage of the large amounts of Motion Capture data accumulated over the years, our aim is to shorten animation production times by providing animators with more control over the specification of high-level parameters and a user-friendly way of retrieving and reusing this data, applying clustering to organize the human motion database and Neural Networks for motion generatio

Similarity, Retrieval, and Classification of Motion Capture Data

Three-dimensional motion capture data is a digital representation of the complex spatio-temporal structure of human motion. Mocap data is widely used for the synthesis of realistic computer-generated characters in data-driven computer animation and also plays an important role in motion analysis tasks such as activity recognition. Both for efficiency and cost reasons, methods for the reuse of large collections of motion clips are gaining in importance in the field of computer animation. Here, an active field of research is the application of morphing and blending techniques for the creation of new, realistic motions from prerecorded motion clips. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is a central topic of this thesis, constitutes a difficult problem due to possible spatio-temporal deformations between logically related motions. Recent approaches to motion retrieval apply techniques such as dynamic time warping, which, however, are not applicable to large data sets due to their quadratic space and time complexity. In our approach, we introduce various kinds of relational features describing boolean geometric relations between specified body points and show how these features induce a temporal segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the relational features and induced segments, we are able to adopt indexing methods allowing for flexible and efficient content-based retrieval in large motion capture databases. As a further application of relational motion features, a new method for fully automatic motion classification and retrieval is presented. We introduce the concept of motion templates (MTs), by which the spatio-temporal characteristics of an entire motion class can be learned from training data, yielding an explicit, compact matrix representation. The resulting class MT has a direct, semantic interpretation, and it can be manually edited, mixed, combined with other MTs, extended, and restricted. Furthermore, a class MT exhibits the characteristic as well as the variational aspects of the underlying motion class at a semantically high level. Classification is then performed by comparing a set of precomputed class MTs with unknown motion data and labeling matching portions with the respective motion class label. Here, the crucial point is that the variational (hence uncharacteristic) motion aspects encoded in the class MT are automatically masked out in the comparison, which can be thought of as locally adaptive feature selection

Human Motion Analysis Using Very Few Inertial Measurement Units

Realistic character animation and human motion analysis have become major topics of research. In this doctoral research work, three different aspects of human motion analysis and synthesis have been explored. Firstly, on the level of better management of tens of gigabytes of publicly available human motion capture data sets, a relational database approach has been proposed. We show that organizing motion capture data in a relational database provides several benefits such as centralized access to major freely available mocap data sets, fast search and retrieval of data, annotations based retrieval of contents, entertaining data from non-mocap sensor modalities etc. Moreover, the same idea is also proposed for managing quadruped motion capture data. Secondly, a new method of full body human motion reconstruction using very sparse configuration of sensors is proposed. In this setup, two sensor are attached to the upper extremities and one sensor is attached to the lower trunk. The lower trunk sensor is used to estimate ground contacts, which are later used in the reconstruction process along with the low dimensional inputs from the sensors attached to the upper extremities. The reconstruction results of the proposed method have been compared with the reconstruction results of the existing approaches and it has been observed that the proposed method generates lower average reconstruction errors. Thirdly, in the field of human motion analysis, a novel method of estimation of human soft biometrics such as gender, height, and age from the inertial data of a simple human walk is proposed. The proposed method extracts several features from the time and frequency domains for each individual step. A random forest classifier is fed with the extracted features in order to estimate the soft biometrics of a human. The results of classification have shown that it is possible with a higher accuracy to estimate the gender, height, and age of a human from the inertial data of a single step of his/her walk

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

The crustal dynamics intelligent user interface anthology

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has, as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI is to develop a friendly and intelligent user interface service based on expert systems and natural language processing technologies. The purpose of such a service is to support the large number of potential scientific and engineering users that have need of space and land-related research and technical data, but have little or no experience in query languages or understanding of the information content or architecture of the databases of interest. This document presents the design concepts, development approach and evaluation of the performance of a prototype IUI system for the Crustal Dynamics Project Database, which was developed using a microcomputer-based expert system tool (M. 1), the natural language query processor THEMIS, and the graphics software system GSS. The IUI design is based on a multiple view representation of a database from both the user and database perspective, with intelligent processes to translate between the views