Motion Clips Research Articles

Active learning for real-time motion controllers

This paper describes an approach to building real-time highly-controllable characters. A kinematic character controller is built on-the-fly during a capture session, and updated after each new motion clip is acquired. Active learning is used to identify which motion sequence the user should perform next, in order to improve the quality and responsiveness of the controller. Because motion clips are selected adaptively, we avoid the difficulty of manually determining which ones to capture, and can build complex controllers from scratch while significantly reducing the number of necessary motion samples.

Active learning for real-time motion controllers

This paper describes an approach to building real-time highly-controllable characters. A kinematic character controller is built on-the-fly during a capture session, and updated after each new motion clip is acquired. Active learning is used to identify which motion sequence the user should perform next, in order to improve the quality and responsiveness of the controller. Because motion clips are selected adaptively, we avoid the difficulty of manually determining which ones to capture, and can build complex controllers from scratch while significantly reducing the number of necessary motion samples.

MCML 기반 모션캡처 데이터 저장 및 퍼지 기반 모션 검색 기법

모션 캡처 기술은 현실감 있는 캐릭터 동작을 얻기 위해 많이 사용되고 있지만, 모션 캡처 데이터의 상이한 포맷들로 인하여 효율적인 모션 데이터의 저장과 검색이 어려운 문제점을 가지고 있다. 본 논문에서는 상이한 형식의 모션 캡처 데이터를 통합하고 효과적으로 저장 및 검객하기 위한 프레임워크를 제안한다. 상이한 모션 캡처 데이터 포맷들을 통합하기 위한 XML 기반의 표준 포맷을 MCML(Motion Capture Markup Language)로 정의하고 있으며 서로 다른 포맷의 모션 캡처 데이터 파일을 하나의 단일화된 MCML 파일로 변환하여 관계형 데이터베이스 또는 XML 데이베이스에 저장함으로써 동일 데이터의 중복 저장 및 공유 문제를 해결한다. 모션캡처 데이터의 검색은 퍼지 문자열 검색(Fuzzy string searching) 기법에 의한 유사어 검색으로 특정 키워드를 포함하는 MCML 문서들을 찾거나 특정 위치의 시작 프레임에서 일련의 프레임들을 선택적으로 추출할 수 있는 모션클립(motion clips) 검색이 가능하도록 하였다. Motion capture technology is widely used for manufacturing animation since it produces high quality character motion similar to the actual motion of the human body. However, motion capture has a significant weakness due to the lack of an industry wide standard for archiving and retrieving motion capture data. In this paper, we propose a framework to integrate, store and retrieve heterogeneous motion capture data files effectively. We define a standard format for integrating different motion capture file formats. Our standard format is called MCML (Motion Capture Markup Language). It is a markup language based on XML (eXtensible Markup Language). The purpose of MCML is not only to facilitate the conversion or integration of different formats, but also to allow for greater reusability of motion capture data, through the construction of a motion database storing the MCML documents. We propose a fuzzy string searching method to retrieve certain MCML documents including strings approximately matched with keywords. The method can be used to retrieve desired series of frames included in MCML documents not entire MCML documents.

Video-guided motion synthesis using example motions

Video taken from a single monocular camera is the most common means of recording human motion. In this article, we present a practical, semiautomatic method for synthesizing a human motion that is guided by such video. After preprocessing an input video, we select a precaptured motion clip called a reference motion from a motion library. We then compute the sequence of body configurations of a virtual character by deforming this motion, according to spacetime constraints derived from a sequence of 2D features in the input video. Experimental results show that our method can synthesize highly dynamic motions, such as kicking and header motions of soccer players. We also showed the potential of our scheme as a new paradigm for motion capture, that is, capturing motions from videos taken with a monocular camera, even outside a motion capture studio.

Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space

This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations --- least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets --- Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.

Photo2Video—A System for Automatically Converting Photographic Series Into Video

A novel method for browsing and sharing a single and series of photographs is presented, which can be regarded as a system exploring a new medium type between photograph and video. The scheme exploits the rich content embedded in a single photograph, as well as in a photographic series. Based on studying the typical process of a viewer's attention to variations on objects or regions in an image, a photograph can be converted into a motion clip by simulating camera motions on it. For a selected photographic series, an appropriate set of key-frames are determined for each photograph based on content analyses. And then camera motion pattern is selected for each photograph to generate a corresponding motion photograph clip. Finally, the final output video is rendered by connecting a series of motion photograph clips with specific transitions based on the content of the images on either side of the transition. Also, each motion photograph clip is aligned with the selected incidental music based on music content analysis. As the system, named Photo2Video, generates motion photographs in a fully automatic or semi-automatic manner, it can be used for increasing efficiency in many applications, such as automatic walkthroughs of photograph galleries, motion photographs on website, electronic greeting cards, and personalized Karaoke

Interactive Virtual Humans in Real-Time Virtual Environment

In this paper, we will present an overview of existing research in the vast area of IVH systems. We will also present our ongoing work on improving the expressive capabilities of IVHs. Because of the complexity of interaction, a high level of control is required over the face and body motions of the virtual humans. In order to achieve this, current approaches try to generate face and body motions from a high-level description. Although this indeed allows for a precise control over the movement of the virtual human, it is difficult to generate a natural-looking motion from such a high-level description. Another problem that arises when animating IVHs is that motions are not generated all the time. Therefore a flexible animation scheme is required that ensures a natural posture even when no animation is playing. We will present MIRAnim, our animation engine, which uses a combination of motion synthesis from motion capture and a statistical analysis of prerecorded motion clips. As opposed to existing approaches that create new motions with limited flexibility, our model adapts existing motions, by automatically adding dependent joint motions. This renders the animation more natural, but since our model does not impose any conditions on the input motion, it can be linked easily with existing gesture synthesis techniques for IVHs. Because we use a linear representation for joint orientations, blending and interpolation is done very efficiently, resulting in an animation engine especially suitable for real-time applications

Entropy‐based motion extraction for motion capture animation

AbstractIn this paper, we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter‐frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system. Copyright © 2005 John Wiley & Sons, Ltd.

Motion retrieval based on movement notation language

AbstractWith the increased availability of motion capture data, the volume of motion library grows so large that it is difficult for animators to manually browse the dataset to search desired motions for reuse. To address this issue, we implement a framework, which allows the user to retrieve motions via Labanotation. For each motion clip in the library, we generate a corresponding Labanotation sequence as additional motion property. A similarity metric for Labanotation sequences is proposed and used to search the motions that have similar Laban descriptions. Our search algorithm is able to retrieve motion segments that only match part of the query Laban sequence. Then based on dynamic programming, these segments are stitched together to form a smooth output motion that is in an optimal sense of matching query Laban sequence. Experimental results demonstrate our method could effectively improve the utilization of motion data. Copyright © 2005 John Wiley & Sons, Ltd.

Automatic generation of human animation based on motion programming

AbstractIn motion simulations, video games and animation films, lots of interactions between characters and virtual environments are needed. Even though realistic motion data can be derived from MoCap system, motion editing and synthesis, animators must adapt these motion data to specific virtual environment manually, which is a boring and time‐consuming job. Here we propose a framework to program the movements of characters and generate navigation animations in virtual environment. Given a virtual environment, a visual user interface is provided for animators to interactively generate motion scripts, describing the characters' movements in this scene and finally used to retrieve motion clips from MoCap database and generate navigation animations automatically. This framework also provides flexible mechanism for animators to get varied resulting animations by configurable table of motion bias coefficients and interactive visual user interface. Copyright © 2005 John Wiley & Sons, Ltd.

Tai Chi synthesizer: a motion synthesis framework based on key‐postures and motion instructions

AbstractHuman spontaneous motions such as walking and runing are seldom described in detail. However, some well designed motions such as conditioning and rehabilitation exercises are usually described in detail and edited into exercise manuals for training purpose. This paper presents a novel framework for synthesizing new motions based on given motion manuals and corresponding motion capture examples. First, using text analysis method, a set of basic motion texts is extracted from exercise manuals and converted into text normal form. Then we introduce an annotation method to build the mapping table between basic motion texts and their corresponding motion clips in the given examples. To facilitate the search for proper motion clips, a multi‐dimensional index structure based on posture parameters is proposed. Then a new motion with given textual description can be synthesized by converting the description sentences into a sequence of text normal forms and then concatenating the corresponding motion clips to form the desired motion. Moreover, to realize the smooth concatenation, we show that it can be achieved by finding an appropriate path in the proposed multi‐dimensional index space. Several experimental examples are given to demonstrate the proposed method is effective in synthesizing desired motions according to given descriptions. Copyright © 2004 John Wiley & Sons, Ltd.

Natural motion animation through constraining and deconstraining at will

This paper presents a computational technique for creating whole-body motions of human and animal characters without reference motion. Our work enables animators to generate a natural motion by dragging a link to an arbitrary position with any number of links pinned in the global frame, as well as other constraints such as desired joint angles and joint motion ranges. The method leads to an intuitive pin-and-drag interface where the user can generate whole-body motions by simply switching on or off or strengthening or weakening the constraints. This work is based on a new interactive inverse kinematics technique that allows more flexible attachment of pins and various types of constraints. Editing or retargeting captured motion requires only a small modification to the original method, although it can also create natural motions from scratch. We demonstrate the usefulness and advantage of our method with a number of example motion clips.

Planning biped locomotion using motion capture data and probabilistic roadmaps

Typical high-level directives for locomotion of human-like characters are useful for interactive games and simulations as well as for off-line production animation. In this paper, we present a new scheme for planning natural-looking locomotion of a biped figure to facilitate rapid motion prototyping and task-level motion generation. Given start and goal positions in a virtual environment, our scheme gives a sequence of motions to move from the start to the goal using a set of live-captured motion clips. Based on a novel combination of probabilistic path planning and hierarchical displacement mapping, our scheme consists of three parts: roadmap construction, roadmap search, and motion generation. We randomly sample a set of valid footholds of the biped figure from the environment to construct a directed graph, called a roadmap, that guides the locomotion of the figure. Every edge of the roadmap is associated with a live-captured motion clip. Augmenting the roadmap with a posture transition graph, we traverse it to obtain the sequence of input motion clips and that of target footprints. We finally adapt the motion sequence to the constraints specified by the footprint sequence to generate a desired locomotion.

ヒューマンフィギュアの全身運動生成のための協応構造化インタフェース

This paper presents an interface for choreographing human and animal characters without reference motion. Its basic function is to enable animators to generate a natural motion clip by dragging a link to an arbitrary position with any number of links pinned in the global frame as well as other constraints such as desired joint angles and joint motion ranges. Each constraint can be switched on or off, strengthened or weakened for each joint at users' will. The interface is based on a new online inverse kinematics technique that allows more flexible attachment of pins and various types of constraints. Although the basic method can create natural motions from scratch, editing or retargetting captured motion requires only a small modification to the original method. We also demonstrate the powerfulness and usability of the proposed method by a number of example motion clips. The same methodology can be applied to flexible and intuitive manipulation of humanoid robots with large degrees of freedom in real time.