Motion Editing Research Articles

Practical Analysis of Digital Technology in Physical Education and Training in the Context of Multimedia Era

Abstract In the multimedia era, the application of digital technology in physical education and training is becoming increasingly important. This paper analyzes how digital technology can improve biological education teaching effectiveness and students’ motivation and independent learning ability. The research methodology uses digital physical education teaching and curriculum integration, motion retrieval technology, and motion editing and scoring technology. It is found that the physical education teaching mode using digital technology is significantly better than the traditional method, in which the advantage of the class using digital technology in the mastery of physical education skills is obvious. The Analysis of final grades shows that the number of experimental courses with more than 90 points is three times as many as that of the control class. The average grade, learning motivation, and independent learning ability are higher than in traditional teaching. The specific data show that the mean value of understanding motivation in the experimental class is 26.47, and that in the control class is 24.87 (P=0.0031), which shows that the digital technology significantly improves students’ motivation to learn. The application of digital technology in physical education can dramatically improve the teaching effect, enhance students’ learning motivation and independent learning ability, an important development direction of modern physical education.

Constructing a Guarantee System for Physical Education Teaching Reform and Innovation in the Digital Era

Abstract In this paper, the world coordinate system transformation in virtual reality technology is utilized to obtain the gesture information for estimating human movement in the process of physical education, and the dynamic sampling light projection algorithm is used to match the gestures of human sports movements. The selection algorithm based on the Q statistic defines the virtual reality technology difference as the error trend of the existence of the difference obtained by each classifier for the new data sports students, and at the same time, combines with the VRT method to construct the VR sports teaching model. In the digital teaching of sports, digital sports teaching materials can be produced by motion editing and video synthesis, and statistical analysis methods can be used to study and analyze the teaching of sports in the digital era. The results show that the highest value recognized by the supervised training algorithm based on JPCD+SRD is 0.92, while the highest value recognized by this paper’s algorithm is 1, i.e., it shows that the effect obtained by this paper’s algorithm is higher than that of supervised training methods, which makes the effect of action recognition in virtual reality technology further improved. The average scores of sit-ups of girls in the control class and the experimental class were improved by 4.03 points and 11.4 points, respectively, compared with those before the experiment, i.e., the use of Internet tools to assist teaching has a significant promotion effect on the sit-up scores of girls. This study facilitates teachers to innovate teaching methods and promotes the process of digital teaching reform in physical education.

ModalNeRF: Neural Modal Analysis and Synthesis for Free‐Viewpoint Navigation in Dynamically Vibrating Scenes

AbstractRecent advances in Neural Radiance Fields enable the capture of scenes with motion. However, editing the motion is hard; no existing method allows editing beyond the space of motion existing in the original video, nor editing based on physics. We present the first approach that allows physically‐based editing of motion in a scene captured with a single hand‐held video camera, containing vibrating or periodic motion. We first introduce a Lagrangian representation, representing motion as the displacement of particles, which is learned while training a radiance field. We use these particles to create a continuous representation of motion over the sequence, which is then used to perform a modal analysis of the motion thanks to a Fourier transform on the particle displacement over time. The resulting extracted modes allow motion synthesis, and easy editing of the motion, while inheriting the ability for free‐viewpoint synthesis in the captured 3D scene from the radiance field. We demonstrate our new method on synthetic and real captured scenes.

Correlation as a measure for alignment and similarity of human motions

AbstractThe ability to measure similarity and alignment of motions is a key tool in motion retrieval and motion editing. Similarity metrics based on distance functions are often utilized when measuring similarity of human motions, however, metrics based on correlation can also potentially useful for measuring similarity and alignment. This paper evaluates the use of correlation as a method of measuring the alignment and similarity of human motion and compares them against more established distance‐based metrics. Three correlation methods and five methods of parameterising rotation are evaluated. The results show that parameterization based on displacement vectors and Kendall Tau rank correlation are optimal for measuring the alignment between two motions. If measuring similarity of motions, however, an approach based on distance metrics for angular or positional distance should be used.

Warping character animations using visual motion features

Despite the wide availability of large motion databases, and recent advances in exploiting them to create tailored contents, artists still spend tremendous efforts in editing and adapting existing character animations to new contexts. This motion editing problem is a well-addressed research topic. Yet only a few approaches have considered the influence of the camera angle, and the resulting visual features it yields, as a mean to control and warp a character animation. This paper proposes the design of viewpoint-dependent motion warping units that perform subtle updates on animations through the specification of visual motion features such as visibility, or spatial extent. We express the problem as a specific case of visual servoing, where the warping of a given character motion is regulated by a number of visual features to enforce. Results demonstrate the relevance of the approach for different motion editing tasks and its potential to empower virtual characters with attention-aware communication abilities.

Multimodal Human Motion Synchronization Dataset

Human motion dataset is an important foundation for researches such as motion data denoising, motion editing, motion synthesis, etc. In order to support more generic studies of multimodal motion data fusion, designing and collecting a public multimodal human motion data set is an urgent problem. First, the acquisition environment is designed for precise motion data collected by sensor-based motion capture devices, rough motion data collected by body sensing devices, and local inertial data collected by inertial measurement units (IMU). Then, temporal synchronization among equipment is applied based on network time protocol (NTP) and spatial synchronization is applied among multi modal data. A full body motion dataset named HFUT-MMD is captured, which contains 6 971 568 frames in 6 types from 12 actors/actresses. The experimental results on the HFUT-MMD dataset using the existing algorithm show that the low precision motion data can be optimized to obtain the motion data similar to the accurate motion data, which corroborates the consistency between the modal data.

Analysis and Evaluation on the Harmfulness of Sports Dance Based on Intelligent Computing

With the rapid development of motion analysis and editing technology, more and more animation technologies based on real human motion analysis are widely used in games, medicine, film and television, sports, and other fields. The role of this technology in the teaching system of sports dance arrangement cannot be ignored. This paper studies the application of intelligent algorithm in injury analysis of sports dance choreography and puts forward a smooth and natural motion editing method which is more in line with the characteristics of human motion. The square spherical surface intelligent algorithm is used to naturally transition the rotation posture of each frame of human joint, and the action modification of computer-aided dance choreography system is more in line with the real action of human body. Simulation results show that interframe data can be used for the same type of dance. Different types of dance arrangement can adopt the method of inserting a new framework between the two dance movements, so as to finally meet the needs of sports dance coaches and students to observe their own dance movements in an all-round way, so as to facilitate the arrangement and modification of sports dance movement design. It provides a certain reference for the application and development of virtual technology in the field of sports and greatly improves the current situation of sports dance teaching equipment. Through animation simulation technology, we can effectively avoid the injury caused by dangerous movements, provide a way of observation and improvement for coaches’ dance teaching, and effectively improve the training effect. Compared with traditional dance choreography methods, the intelligent algorithm in this study can make the animation display of sports dance more coherent and realistic and effectively solve the needs of current events observation and modification in sports dance choreography. It can produce complex movements with simple movements and help teachers arrange and teach sports dance. Avoid the accidental injury caused by the limitation of time and space in traditional teaching and realize the modernization and intelligence of physical education teaching.

Scenario-Based Sensed Human Motion Editing and Validation Through the Motion-Sphere

Synthesizing realistic human motion data using a real-time motion capture system in a controlled environment is a critical challenge. In addition, effectively manipulating the existing motion data is another primary concern and using such modified data in human motion analysis and activity recognition systems are prone to errors. This paper presents a simplistic and comprehensive system to effortlessly author, edit, and validate human motion data. The system enables a naive user to edit the existing motion data interactively using a humanoid model in a three-dimensional space, based on user-defined scenarios and synthesize numerous variations of the motion sequences. A modular concept of scenario-based sensed unit motion editing has been adopted to demonstrate the proposed system. We employed an efficient analytical kinematic and constraint solver to enforce the inherent body joint limits and external constraints while editing to synthesize complete and meaningful motion sequences. Furthermore, we substantiated the proposed sensed unit motion editing framework through a visual validation study using an open-source intuitive visualization tool called the Motion-Sphere. Finally, we compared the resultant synthesized motion against the real-time motion capture system data to verify the body segments’ orientation and position accuracy deviations.

A Survey on Deep Learning for Skeleton‐Based Human Animation

AbstractHuman character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning (DL) and deep reinforcement learning (DRL). In this article, we propose a comprehensive survey on the state‐of‐the‐art approaches based on either DL or DRL in skeleton‐based human character animation. First, we introduce motion data representations, most common human motion datasets and how basic deep models can be enhanced to foster learning of spatial and temporal patterns in motion data. Second, we cover state‐of‐the‐art approaches divided into three large families of applications in human animation pipelines: motion synthesis, character control and motion editing. Finally, we discuss the limitations of the current state‐of‐the‐art methods based on DL and/or DRL in skeletal human character animation and possible directions of future research to alleviate current limitations and meet animators' needs.

Write‐An‐Animation: High‐level Text‐based Animation Editing with Character‐Scene Interaction

Abstract3D animation production for storytelling requires essential manual processes of virtual scene composition, character creation, and motion editing, etc. Although professional artists can favorably create 3D animations using software, it remains a complex and challenging task for novice users to handle and learn such tools for content creation. In this paper, we present Write‐An‐Animation, a 3D animation system that allows novice users to create, edit, preview, and render animations, all through text editing. Based on the input texts describing virtual scenes and human motions in natural languages, our system first parses the texts as semantic scene graphs, then retrieves 3D object models for virtual scene composition and motion clips for character animation. Character motion is synthesized with the combination of generative locomotions using neural state machine as well as template action motions retrieved from the dataset. Moreover, to make the virtual scene layout compatible with character motion, we propose an iterative scene layout and character motion optimization algorithm that jointly considers character‐object collision and interaction. We demonstrate the effectiveness of our system with customized texts and public film scripts. Experimental results indicate that our system can generate satisfactory animations from texts.

A Virtual Modeling Method of Digital Media Image Synchronization Based on Motion Hybrid Algorithm

In virtual reality digital media, motion capture system is used to build basic motion library. Then the basic motion is processed by motion editing technology. Motion blending is one of the most practical and complex editing techniques. This paper proposes a real-time synchronization algorithm based on motion blending, so as to better mix motion dynamically, avoid unexpected effects, and create complex virtual digital media animation. This paper adopts the strategy of data and model hybrid drive. The motion generation and control technology of virtual human is studied from the following aspects: Modeling and simulation method of motion system, grasping of virtual human considering the change of whole body posture, fast generation of operation action, automatic interactive generation of virtual human’s key frame posture, multi priority editing synthesis and interactive control of virtual human’s whole body motion. In this paper, the corresponding control strategy and model are proposed, and the traditional algorithm is improved. Experimental results show that the method improves the efficiency and accuracy of digital media generation. This method provides a reference for the research of digital media image synchronous virtual modeling method based on motion hybrid algorithm.

Interactive multi‐character motion retargeting

AbstractA motion retargeting process is necessary as the body size and proportion of the actors are generally different from those of the target characters. However, the original spatial relationship between the multiple characters and the environment is easily broken when using previous motion retargeting methods, which are generally performed for each character independently. Therefore, time‐consuming manual adjustments by animators are usually required to obtain satisfactory results. To address these issues, we present a novel multicharacter motion retargeting method that preserves various types of spatial relationships between characters and environments. We establish a unified deformation‐based framework for the motion retargeting of multiple characters (more than two) or nonhuman characters with complex interactions. Also, an interactive motion editing interface with immediate feedback to the user is provided. We experimentally show that our method achieves a speedup when compared with previous motion retargeting methods.

Crowd Motion Editing Based on Mesh Deformation

Computer simulation is a significant technology on making great scenes of crowd in the film industry. However, current animation making process of crowd motion requires large manual operations which are time-consuming and inconvenient. To solve the above problem, this paper presents an editing method on the basis of mesh deformation that can rapidly and intuitively edit crowd movement trajectories from the perspective of time and space. The method is applied to directly generate and adjust the crowd movement as well as avoid the crash between crowd and obstacles. As for collisions within the crowd that come along with path modification problem, a time-based solution is put forward to avoid this situation by retaining relative positions of individuals. Moreover, an experiment based on a real venue was performed and the result indicates that the proposed method can not only simplify the editing operations but also improve the efficiency of crowd motion editing.

Stylized human motion warping method based on identity-independent coordinates

Human motion style is a vital concept to virtual human that has a great impact on the expressiveness of the final animation. This paper presents a novel technique that transfers style between heterogeneous motions in real time. Unlike previous approaches, our stylized motion warping is capable of reusing style between heterogeneous motions. The key idea of our work is to represent human motions with identity-independent coordinates (IICs) and learn relative space–time transformations between stylistically different IICs, instead of separating style from content. Once the relative space–time transformations are estimated from a small set of stylized example motions whose contents are identical, our technique is capable of generating style-controllable human motions by applying these transformations to heterogeneous motions with simple linear operations. Experimental results demonstrate that our technique is efficient and powerful in stylized human motion generation. Besides, our technique can also be used in numerous interactive applications, such as real-time human motion style control, stylizing motion graphs and style-based human motion editing.

Optimal and interactive keyframe selection for motion capture

Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.

Human motion data editing based on a convolutional automatic encoder and manifold learning

Technology of human motion capture has been widely used in digital entertainment field. Editing the existing large amount of human motion capture data, correcting and eliminating motion distortion caused by noise and other defects have important value and significance for data reuse. In this paper, data processing is carried out based on convolutional automatic encoder and manifold learning. The popular structure of human motion data was learned by a one-dimensional time domain convolution automatic encoder, in which the hidden unit of the automatic encoder represents motion data. Three constraints were used to overcome the problem that the hidden unit has too much motion editing range. The data to be processed in this paper has no limit on the number of motions. The proposed method can process large data sets in parallel and automatically perform manifold learning without manual labelling and segmentation. In the final, comparative experiments based on a variety of damaged motion data have been carried out. The results showed that the proposed method can effectively reduce the error of the original motion data, and has achieved good results in both objective evaluation and subjective evaluation.

Understanding the impact of animated gesture performance on personality perceptions

Applications such as virtual tutors, games, and natural interfaces increasingly require animated characters to take on social roles while interacting with humans. The effectiveness of these applications depends on our ability to control the social presence of characters, including their personality. Understanding how movement impacts the perception of personality allows us to generate characters more capable of fulfilling this social role. The two studies described herein focus on gesture as a key component of social communication and examine how a set of gesture edits, similar to the types of changes that occur during motion warping, impact the perceived personality of the character. Surprisingly, when based on thin-slice gesture data, people's judgments of character personality mainly fall in a 2D subspace rather than independently impacting the full set of traits in the standard Big Five model of personality. These two dimensions areplasticity, which includes extraversion and openness, andstability, which includes emotional stability, agreeableness, and conscientiousness. A set of motion properties is experimentally determined that impacts each of these two traits. We show that when these properties are systematically edited in new gesture sequences, we can independently influence the character's perceived stability and plasticity (and the corresponding Big Five traits), to generate distinctive personalities. We identify motion adjustments salient to each judgment and, in a series of perceptual studies, repeatedly generate four distinctly perceived personalities. The effects extend to novel gesture sequences and character meshes, and even largely persist in the presence of accompanying speech. This paper furthers our understanding of how gesture can be used to control the perception of personality and suggests both the potential and possible limits of motion editing approaches.

Dance motion capture and composition using multiple RGB and depth sensors

Dynamic human movements such as dance are difficult to capture without using external markers due to the high complexity of a dancer’s body. This article introduces a marker-free motion capture and composition system for dance motion that uses multiple RGB and depth sensors. Our motion capture system utilizes a set of high-speed RGB and depth sensors to generate skeletal motion data from an expert dancer. During the motion acquisition process, a skeleton tracking method based on a particle filter is provided to estimate the motion parameters for each frame from a sequence of color images and depth features retrieved from the sensors. The expert motion data become archived in a database. The authoring methods in our composition system automate most of the motion editing processes for general users by providing an online motion search with an input posture and then performing motion synthesis on an arbitrary motion path. Using the proposed system, we demonstrate that various dance performances can be composed in an intuitive and efficient way on client devices such as tablets and kiosk PCs.

Articulated‐Motion‐Aware Sparse Localized Decomposition

AbstractCompactly representing time‐varying geometries is an important issue in dynamic geometry processing. This paper proposes a framework of sparse localized decomposition for given animated meshes by analyzing the variation of edge lengths and dihedral angles (LAs) of the meshes. It first computes the length and dihedral angle of each edge for poses and then evaluates the difference (residuals) between the LAs of an arbitrary pose and their counterparts in a reference one. Performing sparse localized decomposition on the residuals yields a set of components which can perfectly capture local motion of articulations. It supports intuitive articulation motion editing through manipulating the blending coefficients of these components. To robustly reconstruct poses from altered LAs, we devise a connection‐map‐based algorithm which consists of two steps of linear optimization. A variety of experiments show that our decomposition is truly localized with respect to rotational motions and outperforms state‐of‐the‐art approaches in precisely capturing local articulated motion.

Data-driven inverse dynamics for human motion

Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.