Figure 1: Real-time physics-based simulation of walking. The method provides robust control across a range of gaits, styles, characters, and skills. Motions are easily authored by novice users. We present a control strategy for physically-simulated walking motions that generalizes well across gait parameters, motion styles, character proportions, and a variety of skills. The control is realtime, requires no character-specific or motion-specific tuning, is robust to disturbances, and is simple to compute. The method works by integrating tracking, using proportional-derivative control; foot placement, using an inverted pendulum model; and adjustments for gravity and velocity errors, using Jacobian transpose control. Highlevel gait parameters allow for forwards-and-backwards walking, various walking speeds, turns, walk-to-stop, idling, and stop-towalk behaviors. Character proportions and motion styles can be authored interactively, with edits resulting in the instant realization of a suitable controller. The control is further shown to generalize across a variety of walking-related skills, including picking up objects placed at any height, lifting and walking with heavy crates, pushing and pulling crates, stepping over obstacles, ducking under obstacles, and climbing steps. 1

Computer animation, virtual human, motion blending, motion adaptation. Virtual humans are more and more used in VR applications but their animation is still a challenge, especially if complex tasks must be carried-out in interaction with the user. In many applications with virtual humans, credible virtual characters play a major role in presence. Motion editing techniques assume that the natural laws are intrinsically encoded in prerecorded trajectories and that modifications may preserve them leading to credible autonomous actors. However, a complete knowledge of all the constraints is required to ensure continuity or to synchronize and blend several actions necessary to achieve a given task. We propose a framework capable of performing these tasks in an interactive environment that can change at each frame, depending on the user’s orders. This framework enables to animate from dozens of characters in real-time for complex constraints to hundreds of characters if only ground adaptation is performed. It offers the following capabilities: motion synchronization, blending, retargeting and adaptation thanks to enhanced inverse kinetics and kinematics solver. To evaluate this framework we have compared the motor behavior of subjects in real and in virtual environments.

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Abstract. Humanoid 3D models can be easily acquired through various sources, including online. The use of such models within a game or simulation environment requires human input and intervention in order to associate such a model with a relevant set of motions and control mechanisms. In this paper, we demonstrate a pipeline where humanoid 3D models can be incorporated within seconds into an animation system, and infused with a wide range of capabilities, such as locomotion, object manipulation, gazing, speech synthesis and lip syncing. We offer a set of heuristics that can associated arbitrary joint names with canonical ones, and describe an fast retargeting algorithm that enables us to instill a set of behaviors onto an arbitrary humanoid skeleton. We believe that such a system will vastly increase the use of 3D interactive characters due to the ease that new models can be animated.

We introduce a constraint-based motion editing technique enforcing the intrinsic motion flow of a given motion pattern (e.g., golf swing). Its major characteristic is to operate in the motion Principal Coefficients (PCs) space instead of the pose PCs space. By construction, it is sufficient to constrain a single frame with Inverse Kinematics (e.g., the hitting position of the golf club head) to obtain a motion solution preserving the motion pattern style. Such an approach also reduces the size of the Jacobian matrix as the motion PCs space dimension is small for a coordinated movement (e.g. up to 9 for the golf swing illustrating this paper). We compare this approach against a per-frame prioritized IK method regarding performance, expressivity and simplicity.

This thesis presents Motion Curve space: a novel representation scheme for the poses of an articulated skeletal figure. A Motion Curve space is defined by a set of orthogonal basis vectors that have been found by performing a weighted principal component analysis on an example motion clip. An animator can control the properties of the space through the selection of the example clip and the PCA weights. We explore the expressive and computational power of the representation through the creation of several new motion processing and analysis algorithms, which are demonstrated through prototype applications. These prototypes help to establish the workflow for a hypothetical production application. In presenting this work, we hope to expand the size of the animator’s toolbox. By providing a new and usable framework for editing motions, we make it possible to quickly modify existing motion assets and stretch animation budgets.

Figure 1: Example frames from distinctive (leftmost pairs) and non-distinctive walking, jogging and dancing motion clips. Recent advances in rendering and data-driven animation have en-abled the creation of compelling characters with impressive levels of realism. While data-driven techniques can produce animations that are extremely faithful to the original motion, many challenging problems remain because of the high complexity of human motion. A better understanding of the factors that make human motion rec-ognizable and appealing would be of great value in industries where creating a variety of appealing virtual characters with realistic mo-tion is required. To investigate these issues, we captured thirty ac-tors walking, jogging and dancing, and applied their motions to the same virtual character (one each for the males and females). We then conducted a series of perceptual experiments to explore the distinctiveness and attractiveness of these human motions, and whether characteristic motion features transfer across an individ-ual’s different gaits. Average faces are perceived to be less distinc-tive but more attractive, so we explored whether this was also true for body motion. We found that dancing motions were most easily recognized and that distinctiveness in one gait does not predict how recognizable the same actor is when performing a different motion. As hypothesized, average motions were always amongst the least distinctive and most attractive. Furthermore, as 50 % of participants in the experiment were Caucasian European and 50 % were Asian Korean, we found that the latter were as good as or better at rec-ognizing the motions of the Caucasian actors than their European counterparts, in particular for dancing males, whom they also rated more highly for attractiveness.

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Abstract. Nowadays, crowds of virtual characters are used in many domains such as neurosciences, psychology, and computer sciences. Since as human beings, we are natural experts in human being representation and movement, it makes it that much harder to correctly model and animate virtual characters. This becomes even more challenging when considering crowds of virtual characters. Indeed, in addition to the representation and animation, there is the mandatory trade-off between rich, realistic behaviors and computational costs. In this paper, we present a crowd engine, to which we introduce and extra layer which allows its characters to produce gaze behaviors. We thus enhance crowd realism by allowing the characters composing it to be aware of their environment and other characters and/or a user. 1