International audience

Abstract Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing ( $$N=292$$ N = 292 ); we study the users’ motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user’s motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications.

We explore a new strategy for few-shot novel view synthesis based on a neural light field representation. Given a target camera pose, an implicit neural network maps each ray to its target pixel’s color directly. The network is conditioned on local ray features generated by coarse volumetric rendering from an explicit 3D feature volume. This volume is built from the input images using a 3D ConvNet. Our method achieves competitive performances on real MVS data with respect to state-of-the-art neural radiance field based competition, while offering a roughly 50 times faster rendering.

This paper aims to improve neural radiance fields (NeRF) from sparse inputs. NeRF achieves photo-realistic renderings when given dense inputs, while its’ performance drops dramatically with the decrease of training views’ number. Our insight is that the standard volumetric rendering of NeRF is prone to over-fitting due to the lack of overall geometry and local neighborhood information from limited inputs. To address this issue, we propose a global sampling strategy with a geometry regularization utilizing warped images as augmented pseudo-views to encourage geometry consistency across multi-views. In addition, we introduce a local patch sampling scheme with a patch-based regularization for appearance consistency. Furthermore, our method exploits depth information for explicit geometry regularization. The proposed approach outperforms existing baselines on real benchmarks DTU datasets from sparse inputs and achieves the state of art results.

Defining equivalences between poses of different human characters is an important problem for imitation research, human pose recognition and deformation transfer. However, pose equivalence is a subjective information that depends on context and on the morphology of the characters. A common hypothesis is that interactions between body surfaces, such as self-contacts, are important attributes of human poses, and are therefore consistently included in animation approaches aiming at retargeting human motions. However, some of these self-contacts are only present because of the morphology of the character and are not important to the pose, e.g. contacts between the upper arms and the torso during a standing A-pose. In this paper, we conduct a first study towards the goal of understanding the impact of self-contacts between body surfaces on perceived pose equivalences. More specifically, we focus on contacts between the arms or hands and the upper body, which are frequent in everyday human poses. We conduct a study where we present to observers two models of a character mimicking the pose of a source character, one with the same self-contacts as the source, and one with one self-contact removed, and ask observers to select which model best mimics the source pose. We show that while poses with different self-contacts are considered different by observers in most cases, this effect is stronger for self-contacts involving the hands than for those involving the arms.

We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters. Traditional methods that tackle this problem assume a human pose model to be available and transfer poses between characters using this model. In this work, we take a different approach and transform the identity of a character into a new identity without modifying the character’s pose. This offers the advantage of not having to define equivalences between 3D human poses, which is not straightforward as poses tend to change depending on the identity of the character performing them, and as their meaning is highly contextual. To achieve the deformation transfer, we propose a neural encoder-decoder architecture where only identity information is encoded and where the decoder is conditioned on the pose. We use pose independent representations, such as isometry-invariant shape characteristics, to represent identity features. Our model uses these features to supervise the prediction of offsets from the deformed pose to the result of the transfer. We show experimentally that our method outperforms state-of-the-art methods both quantitatively and qualitatively, and generalises better to poses not seen during training. We also introduce a fine-tuning step that allows to obtain competitive results for extreme identities, and allows to transfer simple clothing.

We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features. Given a single input color image, existing graph convolutional network (GCN) based techniques for human shape and pose estimation (e.g. [19]) use a single convolutional neural network (CNN) generated global image feature appended to all mesh vertices equally to initialize the GCN stage, which transforms a template T-posed mesh into the target pose. In contrast, we propose for the first time the idea of using local image features per vertex. These features are sampled from the CNN image feature maps by utilizing pixel-to-mesh correspondences generated with DensePose [11]. Our quantitative and qualitative results on standard benchmarks show that using local features improves on global ones and leads to competitive performances with respect to the state-of-the-art.

While digital humans are key aspects of the rapidly evolving areas of virtual reality, gaming, and online communications, many applications would benefit from using digital personalized (stylized) representations of users, as they were shown to highly increase immersion, presence and emotional response. In particular, depending on the target application, one may want to look like a dwarf or an elf in a heroic fantasy world, or like an alien on another planet, in accordance with the style of the narrative. While creating such virtual replicas requires stylization of the user’s features onto the virtual character, no formal study has however been conducted to assess the ability to recognize stylized characters. In this paper, we present a perceptual study investigating the effect of the degree of stylization on the ability to recognize an actor, and the subjective acceptability of stylizations. Results show that recognition rates decrease when the degree of stylization increases, while acceptability of the stylization increases. These results provide recommendations to achieve good compromises between stylization and recognition, and pave the way to new stylization methods providing a tradeoff between stylization and recognition of the actor.

Retargeting a motion from a source to a target character is an important problem in computer animation, as it allows to reuse existing rigged databases or transfer motion capture to virtual characters. Surface based pose transfer is a promising approach to avoid the trial-and-error process when controlling the joint angles. The main contribution of this paper is to investigate whether shape transfer instead of pose transfer would better preserve the original contextual meaning of the source pose. To this end, we propose an optimization-based method to deform the source shape+pose using three main energy functions: similarity to the target shape, body part volume preservation, and collision management (preserve existing contacts and prevent penetrations). The results show that our method is able to retarget complex poses, including several contacts, to very different morphologies. In particular, we introduce new contacts that are linked to the change in morphology, and which would be difficult to obtain with previous works based on pose transfer that aim at distance preservation between body parts. These preliminary results are encouraging and open several perspectives, such as decreasing computation time, and better understanding how to model pose and shape constraints.

Retargeting motion from one character to another is a key process in computer animation. It enables to reuse animations designed for a character to animate another one, or to make performance-driven be faithful to what has been performed by the user. Previous work mainly focused on retargeting skeleton animations whereas the contextual meaning of the motion is mainly linked to the relationship between body surfaces, such as the contact of the palm with the belly. In this paper we propose a new context-aware motion retargeting framework, based on deforming a target character to mimic a source character poses using harmonic mapping. We also introduce the idea of Context Graph: modeling local interactions between surfaces of the source character, to be preserved in the target character, in order to ensure fidelity of the pose. In this approach, no rigging is required as we directly manipulate the surfaces, which makes the process totally automatic. Our results demonstrate the relevance of this automatic rigging-less approach on motions with complex contacts and interactions between the character's surface.

Human-like characters can be modeled by suitable skeletal structures, which basically consist in trees where edges represent bones and vertices are joints between two adjacent bones.Motion is then defined as variations of the joints' configuration (i.e., partial rotations) over time, which also influences joint positions.However, this representation does not allow to easily represent the relationship between joints that are not directly connected by a bone.This work is therefore based on the premise that variations of the relative distances between such joints are important to represent complex human motions.While the former representations are currently used in practice for playing and analyzing motions, the latter can help in modeling a new class of problems where the relationships in human motions need to be simulated.Our main interest in this work is in adapting previously captured human postures (one frame of a given motion) with the aim of satisfying a certain number of geometrical constraints, which turn out to be easily definable in terms of distances.We present a novel procedure for approximating the relative inter-joint distances for skeletal structures having arbitrary features and respecting a predefined posture.This set of inter-joint distances defines an instance of the Distance Geometry Problem (DGP), that we tackle with a non-monotone spectral gradient method.

Exergames involve using the fullbody to interact with an immersive world, which raises the challenge of capturing, processing and recognizing the action of the user even for cheap mocap systems such as the Microsoft Kinect. In fact, these recent technological advances have renewed interest in skeleton-based action recognition. Our review of related literature reveals that the issues encountered are not the result of random processes, which could simply be studied by using statistical tools, but are instead due to the fact that the pattern to be recognized, i.e. an action, was produced by a human being. 2D hand-drawn symbols are further examples of patterns resulting from a human motion. Therefore, the main contribution of this paper is to examine the validity of transferring the expertise of hand-drawn symbol representation to better recognize actions based on skeleton data. Principally, we propose a new action representation, namely the 3DMM, as an initial case-study illustrating how such transfer could be conducted. The experimental results, obtained over two benchmarks, confirm the soundness of our approach and encourage more thorough examination of the transfer.

In character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphology-independent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.

Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database of accurately captured human poses as a prior to optimize the Kinect recognized ones, in order to estimate the true poses performed by the user. The key research problem is to identify the most relevant poses in the database for accurate and efficient reconstruction. In this paper, we propose a new pose reconstruction method based on modelling the pose database with a structure called Filtered Pose Graph, which indicates the intrinsic correspondence between poses. Such a graph not only speeds up the database poses selection process, but also improves the relevance of the selected poses for higher quality reconstruction. We apply the proposed method in a challenging environment of industrial context that involves sub-optimal Kinect placement and a large amount of occlusion. Experimental results show that our real-time system reconstructs Kinect poses more accurately than existing methods.

Action recognition based on human skeleton structure represents nowadays a prosper research field. This is mainly due to the recent advances in terms of capture technologies and skeleton extraction algorithms. In this context, we observed that 3D skeleton-based actions share several properties with handwritten symbols since they both result from a human performance. We accordingly hypothesize that the action recognition problem can take advantage of trial and error already carried out on handwritten patterns. Therefore, inspired by one of the most efficient and compact handwriting feature-set, we propose in this paper a skeleton descriptor referred to as Handwriting-Inspired Features (HIF3D). First of all a data preprocessing is applied to joint trajectories in order to handle the variabilities among actor's morphologies. Then we extract the HIF3D features from the processed joint locations according to a time partitioning scheme so as to additionally encode the temporal information over the sequence. Finally, we selected the Support Vector Machine (SVM) to achieve the classification step. Evaluations conducted on two challenging datasets, namely HDM05 and UTKinect, testify the soundness of our approach as the obtained results outperform the state-of-the-art algorithms that rely on skeleton data.

We present an interactive motion planning algorithm to compute plausible trajectories for high-DOF human-like characters. Given a discrete sequence of contact configurations, we use a three-phase optimization approach to ensure that the resulting trajectory is collision-free, smooth, and satisfies dynamic balancing constraints. Our approach can directly compute dynamically balanced and natural-looking motions at interactive frame rates and is considerably faster than prior methods. We highlight its performance on complex human motion benchmarks corresponding to walking, climbing, crawling, and crouching, where the discrete configurations are generated from a kinematic planner or extracted from motion capture datasets.

In this paper, we propose a novel idea to address the problem of fast computation of stable force-closure grasp configurations for a multifingered hand and a 3-D rigid object represented as a polygonal soup model. The proposed method performs a low-level shape exploration by wrapping multiple cords around the object in order to quickly isolate promising grasping regions. Around these regions, we compute grasp configurations by applying a variant of the close-until-contact procedure to find the contact points. The finger kinematics and the contact information are then used to filter out unstable grasps. Through many simulated examples with three different anthropomorphic hands, we demonstrate that, compared with previous grasp planners such as the generic grasp planner in Simox, the proposed grasp planner can synthesize grasps that are more natural-looking for humans (as measured by the grasp quality measure skewness) for objects with complex geometries in a short amount of time. Unlike many other planners, this is achieved without costly model preprocessing such as segmentation by parts and medial axis extraction.

The use of Virtual Reality (VR) in sports training is now widely studied with the perspective to transfer motor skills learned in virtual environments (VEs) to real practice. However precision motor tasks that require high accuracy have been rarely studied in the context of VE, especially in Large Screen Image Display (LSID) platforms. An example of such a motor task is the basketball free throw, where the player has to throw a ball in a 46cm wide basket placed at 4.2m away from her. In order to determine the best VE training conditions for this type of skill, we proposed and compared three training paradigms. These training conditions were used to compare the combinations of different user perspectives: first (1PP) and third-person (3PP) perspectives, and the effectiveness of visual guidance. We analysed the performance of eleven amateur subjects who performed series of free throws in a real and immersive 1:1 scale environment under the proposed conditions. The results show that ball speed at the moment of the release in 1PP was significantly lower compared to real world, supporting the hypothesis that distance is underestimated in large screen VEs. However ball speed in 3PP condition was more similar to the real condition, especially if combined with guidance feedback. Moreover, when guidance information was proposed, the subjects released the ball at higher - and closer to optimal - position (5-7% higher compared to no-guidance conditions). This type of information contributes to better understand the impact of visual feedback on the motor performance of users who wish to train motor skills using immersive environments. Moreover, this information can be used by exergames designers who wish to develop coaching systems to transfer motor skills learned in VEs to real practice.

ABSTRACT Humans adjust their movements in advance to prepare for the forthcoming action, resulting in efficient and smooth transitions. However, traditional computer animation approaches such as motion graphs simply concatenate a series of actions without taking into account the following one. In this paper, we propose a new method to produce preparation behaviors using reinforcement learning. As an offline process, the system learns the optimal way to approach a target and to prepare for interaction. A scalar value called the level of preparation is introduced, which represents the degree of transition from the initial action to the interacting action. To synthesize the movements of preparation, we propose a customized motion blending scheme based on the level of preparation, which is followed by an optimization framework that adjusts the posture to keep the balance. During runtime, the trained controller drives the character to move to a target with the appropriate level of preparation, resulting in a humanlike behavior. We create scenes in which the character has to move in a complex environment and to interact with objects, such as crawling under and jumping over obstacles while walking. The method is useful not only for computer animation but also for real‐time applications such as computer games, in which the characters need to accomplish a series of tasks in a given environment. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper we study how the visual animation of a self-avatar can be artificially modified in real-time in order to generate different haptic perceptions. In our experimental setup, participants could watch their self-avatar in a virtual environment in mirror mode while performing a weight lifting task. Users could map their gestures on the self-animated avatar in real-time using a Kinect. We introduce three kinds of modification of the visual animation of the self-avatar according to the effort delivered by the virtual avatar: 1) changes on the spatial mapping between the user’s gestures and the avatar, 2) different motion profiles of the animation, and 3) changes in the posture of the avatar (upper-body inclination). The experimental task consisted of a weight lifting task in which participants had to order four virtual dumbbells according to their virtual weight. The user had to lift each virtual dumbbells by means of a tangible stick, the animation of the avatar was modulated according to the virtual weight of the dumbbell. The results showed that the altering the spatial mapping delivered the best performance. Nevertheless, participants globally appreciated all the different visual effects. Our results pave the way to the exploitation of such novel techniques in various VR applications such as sport training, exercise games, or industrial training scenarios in single or collaborative mode.

In this paper, we propose a novel idea to address the problem of fast computation of enveloping grasp configurations for a multi-fingered hand with 3D polygonal models represented as polygon soups. The proposed method performs a low-level shape matching by wrapping multiple cords around an object in order to quickly isolate promising grasping spots. From these spots, hand palm posture can be computed followed by a standard close-until-contact procedure to find the contact points. Along with the contacts information, the finger kinematics is then used to filter the unstable grasps. Through multiple simulated examples with a twelve degrees-of-freedom anthropomorphic hand, we demonstrate that our method can compute good grasps for objects with complex geometries in a short amount of time. Best of all, this is achieved without complex model preprocessing like segmentation by parts and medial axis extraction.

Recognition of natural gestures is a key issue in many applications including videogames and other immersive applications. Whatever is the motion capture device, the key problem is to recognize a motion that could be performed by a range of different users, at an interactive frame rate. Hidden Markov Models (HMM) that are commonly used to recognize the performance of a user however rely on a motion representation that strongly affects the overall recognition rate of the system. In this paper, we propose to use a compact motion representation based on Morphology-Independent features and we evaluate its performance compared to classical representations. When dealing with 15 very similar upper limb motions, HMM based on Morphology-Independent features yield significantly higher recognition rate (84.9%) than classical Cartesian or angular data (70.4% and 55.0%, respectively). Moreover, when the unknown motions are performed by a large number of users who have never contributed to the learning process, the recognition rate of Morphology-Independent input feature only decreases slightly (down to 68.2% for a HMM trained with the motions of only one subject) compared to other features (25.3% for Cartesian features and 17.8% for angular features in the same conditions). The method is illustrated through an interactive demo in which three virtual humans have to interactively recognize and replay the performance of the user. Each virtual human is associated with a HMM recognizer based on the three different input features.

In this paper we introduce novel 'Camera Motions' (CMs) to improve the sensations related to locomotion in virtual environments (VE). Traditional Camera Motions are artificial oscillating motions applied to the subjective viewpoint when walking in the VE, and they are meant to evoke and reproduce the visual flow generated during a human walk. Our novel camera motions are: (1) multistate, (2) personified, and (3) they can take into account the topography of the virtual terrain. Being multistate, our CMs can account for different states of locomotion in VE namely: walking, but also running and sprinting. Being personified, our CMs can be adapted to avatar's physiology such as to its size, weight or training status. They can then take into account avatar's fatigue and recuperation for updating visual CMs accordingly. Last, our approach is adapted to the topography of the VE. Running over a strong positive slope would rapidly decrease the advance speed of the avatar, increase its energy loss, and eventually change the locomotion mode, influencing the visual feedback of the camera motions. Our new approach relies on a locomotion simulator partially inspired by human physiology and implemented for a real-time use in Desktop VR. We have conducted a series of experiments to evaluate the perception of our new CMs by naive participants. Results notably show that participants could discriminate and perceive transitions between the different locomotion modes, by relying exclusively on our CMs. They could also perceive some properties of the avatar being used and, overall, very well appreciated the new CMs techniques. Taken together, our results suggest that our new CMs could be introduced in Desktop VR applications involving first-person navigation, in order to enhance sensations of walking, running, and sprinting, with potentially different avatars and over uneven terrains, such as for: training, virtual visits or video games.

According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.

Perception of biological motions is a key issue in order to evaluate the quality and the credibility of motions of virtual humans. This paper presents a perceptual study to evaluate if human beings are able to accurately distinguish differences in natural lifting motions with various masses in virtual environments (VE), which is not the case. However, they reached very close levels of accuracy when watching to computer animations compared to videos. Still, quotes of participants suggest that the discrimination process is easier in videos of real motions which included muscles contractions, more degrees of freedom, etc. These results can be used to help animators to design efficient physically-based animations.

The Walking-In-Place interaction technique was introduced to navigate infinitely in 3D virtual worlds by walking in place in the real world. The technique has been initially developed for users standing in immersive setups and was built upon sophisticated visual displays and tracking equipments.

Abstract This paper presents a novel, parameteric framework for synthesizing new character motions from existing motion capture data. Our framework can conduct morphological adaptation as well as kinematic and physically‐based corrections. All these solvers are organized in layers in order to be easily combined together. Given locomotion as an example, the system automatically adapts the motion data to the size of the synthetic figure and to its environment; the character will correctly step over complex ground shapes and counteract with external forces applied to the body. Our framework is based on a frame‐based solver. This ensures animating hundreds of humanoids with different morphologies in real‐time. It is particularly suitable for interactive applications such as video games and virtual reality where a user interacts in an unpredictable way. Copyright © 2009 John Wiley & Sons, Ltd.

This paper aims at proposing a framework for animating virtual humans that can efficiently interact with real users in virtual reality (VR). If the user's order can be modeled as targets and commands, the system searches a database for the most convenient behavior. In order to avoid using a huge database that can deal with any kind of situation, we propose to associate this searching process to an adaptation module. Hence, even if the selected motion is not perfectly suited with the situation, it can be adapted in order to reach accurately the target specified by the user. This framework is illustrated with a kung-fu fighter example. Two people are involved in this example: the user and the supervisor. The user is displacing in the real environment while the position of his head is tracked in real-time thanks to reflective markers. The virtual opponent follows the displacement of the user to stay close to him. At any time, the supervisor can ask the virtual character to kick or punch the user. Our system automatically searches for the convenient motion in an average-size database (75 motions compared to hundreds of motions required for motion graphs) and adapts it to the current situation.

This paper aims at proposing a new method to animate aerial motions in interactive environments while taking dynamics into account. Classical approaches are based on spacetime constraints and require a complete knowledge of the motion. However, in Virtual Reality, the user's actions are unpredictable so that such techniques cannot be used. In this paper, we deal with the simulation of gymnastic aerial motions in virtual reality. A user can directly interact with the virtual gymnast thanks to a real-time motion capture system. The user's arm motions are blended to the original aerial motions in order to verify their consequences on the virtual gymnast's performance. Hence, a user can select an initial motion, an initial velocity vector, an initial angular momentum, and a virtual character. Each of these choices has a direct influence on mechanical values such as the linear and angular momentum. We thus have developed an original method to adapt the character's poses at each time step in order to make these values compatible with mechanical laws: the angular momentum is constant during the aerial phase and the linear one is determined at take-off. Our method enables to animate up to 16 characters at 30hz on a common PC. To sum-up, our method enables to solve kinematic constraints, to retarget motion and to correct it to satisfy mechanical laws. The virtual gymnast application described in this paper is very promising to help sports-men getting some ideas which postures are better during the aerial phase for better performance.

This project aims at the creation of a virtual humanoid endowed with expressive gestures. More specifically, we focus our attention on expressiveness (what type of gesture: fluidity, tension, anger) and on its semantic representations. Our approach relies on a data-driven animation scheme. From motion data captured thanks to an optical system and data gloves, we try to extract significant features of communicative gestures, and to re-synthesize them afterward with style variation. The proposed model is applied to the generation of a set of French sign language (FSL) gestures. Within this framework, a database involving the whole body, hands motion and facial expressions has been built The analysis of this database makes possible information retrieval about the semantics as well as the execution style of FSL gestures. These characteristics are integrated in gesture synthesis models qualitatively evaluated by their intelligibility and the realism of the produced animations.

Abstract In this paper we address the problem of temporal alignment applied to capture communicative gestures conveying different styles. We propose a representation space that may be considered asrobustto the spatial variability induced by style. By extending a multilevel dynamic time warping algorithm, we show how this extension can fulfil the goals of time correspondence between gesture sequences while preventing jerkiness introduced by standard time warping methods. Copyright © 2006 John Wiley & Sons, Ltd.

International audience

Using motion capture systems to animate humanlike figures still remains difficult when the movements are complex or need to be adapted to geometric constraints. We propose a new method to blend several captured movements while adapting the trajectories to new skeletons and to unknown environments. For each body part (considered as resources), a priority is defined for each movement (considered as consumers). The trajectories applied to the skeleton consist of a weighted sum of the motions trajectories. A new technique to compute the weights is proposed. Finally, the system adapts the resulting trajectories to the synthetic skeleton and to the environment. The results enabled to animate up to one hundred actors in interactive environments.

The paper introduces a novel method for the coordination of human motion based on planification and AI techniques. Motions are considered as black boxes that are activated according to pre-conditions and produce post-conditions in a hybrid continuous and discrete world. Each part of the body is an autonomous entity which cooperates with the others depending on global criteria such as occupation rate and distance to a goal (common to all the entities). This technique makes it possible to easily specify and solve the motion coordination problem of a juggler that deals with a dynamic number of balls in real time.

This work presents a simulation system designed to carry-out virtual experiments on human motion. 3D visualization, automatic code generation and generic control design patterns provide biomechanicians and medics with dynamic simulation tools. The paper first deals with the design of mechanical models of human beings. It also presents design patterns of controllers for an upper-limb model composed of 11 degrees of freedom. As an example, two controllers are presented in order to illustrate these design patterns. The paper also presents a user-friendly interface dedicated to medics that makes it possible to enter orders in natural language.

This paper surveys the set of techniques developed in computer graphics for animating human walking. First we focus on the evolution from purely kinematic ‘knowledge-based’ methods to approaches that incorporate dynamic constraints or use dynamic simulations to generate motion. Then we review the recent advances in motion editing that enable the control of complex animations by interactively blending and tuning synthetic or captured motions. Copyright © 1999 John Wiley & Sons, Ltd.