Teleportation is one of the most common locomotion techniques in virtual reality, yet its perceptual properties remain underexplored. While redirected walking research has shown that users’ movements can be subtly manipulated without detection, similar imperceptible adjustments for teleportation have not been systematically investigated. This study examines the thresholds at which teleportation displacements become noticeable to users. We conducted a repeated-measures experiment in which participants’ selected teleport destinations were altered in both direction (forwards, backwards) and at different ranges (small, large). Detection thresholds for these positional adjustments were estimated using a psychophysical staircase method with a two-alternative forced choice (2AFC) task.Results show that teleport destinations can be shifted without detection, with larger tolerances for backward adjustments and across longer teleport ranges. These findings establish baseline perceptual limits for redirected teleportation and highlight its potential as a design technique. Applications include supporting interpersonal distance management in social VR, guiding players toward objectives in games, and assisting novice users with navigation. By identifying the limits of imperceptible teleportation adjustments, this work extends redirection principles beyond walking to teleportation and opens new opportunities for adaptive and socially aware VR locomotion systems.

We present an extension to traditional rig skinning, like Linear Blend Skinning (LBS), to produce secondary motions that exhibit the appearance of a physical phenomena without need for simulation. At the core of the technique, we call dynamic skinning, is a set of deformers which offset position of individual vertices as a function of position derivatives and time. Examples of such deformers create effects such as oscillation in response to movement and the appearance of wave propagation, among others. Because the technique computes offsets directly and does not solve physics equations, it is extremely fast to compute. It also boasts a highdegree of customizability which supports a desirable artist workflow and fine level of control. Finally, we showcase the technique in a number of scenarios and make comparisons with the state of the art.

We present an approach for training “agile” character control policies, able to produce a wide variety of motor skills from a single reference motion cycle. Our technique builds off of generative adversarial imitation learning (GAIL), with a key novelty of our approach being to provide modification to the observation map in order to improve agility and robustness. Namely, to support more agile behavior, we adjust the value measurements of the training discriminator through relative features - hence the name ReGAIL. Our state observations include both task relevant relative velocities and poses, as well as relative goal deviation information. In addition, to increase robustness of the resulting gaits, servo gains and damping values are included as part of the policy action to let the controller learn how to best combine tension and relaxation during motion. From a policy informed by a single reference motion, our resulting agent is able to maneuver as needed, at runtime, from walking forward to walking backward or sideways, turning and stepping nimbly. Moreover, thanks to the use of observations in relative frames, the trained controllers are robust to morphological changes of the simulated character, which makes adaptation to new morphologies straightforward. We demonstrate our approach for a humanoid and a quadruped, on both flat and sloped terrains, as well as provide ablation studies to validate the design choices of our framework. In addition, we present an application to prehistoric research, where being able to simulate hominids of specific morphologies on rough terrain is valuable with encouraging results.

Abstract This work presents a method to control the output of mesh simplification algorithms based on iterative edge collapses. Traditional mesh simplification focuses on preserving the visual appearance. Despite still being an important criterion, other geometric properties also play critical roles in different applications, such as triangle quality for computations. This motivates our work to stay under the umbrella of the popular quadric error mesh simplification, while proposing different ways to control the simplified mesh to possess other geometric properties. The key ingredient of our work is another quadric error, called line quadrics, which can be seamlessly added to the vanilla quadric error metric. We show that, theoretically and empirically, adding our line quadrics can improve the numerics and encourage the simplified mesh to have uniformly distributed vertices. If we spread the line quadric adaptively to different regions, it can easily lead to soft preservation of feature vertices and edges. Our method is simple to implement, requiring only a few lines of code change on top of the original quadric error simplification, and can lead to a variety of user controls.

Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless policy composition and transitions between skills. A long-horizon task typically consists of distinct subtasks with well-defined goals, separated by transitional subtasks with unclear goals but critical to the success of the entire task. Existing methods like the mixture of experts and skill chaining struggle with tasks where individual policies do not share significant commonly explored states or lack well-defined initial and terminal states between different phases. In this paper, we introduce a novel policy integration framework to enable the composition of drastically different motor skills in multi-phase long-horizon tasks with ill-defined intermediate states. Based on that, we further introduce a high-level soft router to enable seamless and robust transitions between the subtasks. We evaluate our framework on a set of fundamental basketball skills and challenging transitions. Policies trained by our approach can effectively control the simulated character to interact with the ball and accomplish the long-horizon task specified by real-time user commands, without relying on ball trajectory references.

As social VR grows in popularity, understanding how to optimise interactions becomes increasingly important. Interpersonal distance-the physical space people maintain between each other-is a key aspect of user experience. Previous work in psychology has shown that breaches of personal space cause stress and discomfort. Thus, effectively managing this distance is crucial in social VR, where social interactions are frequent. Teleportation, a commonly used locomotion method in these environments, involves distinct cognitive processes and requires users to rely on their ability to estimate distance. Despite its widespread use, the effect of teleportation on proximity remains unexplored. To investigate this, we measured the interpersonal distance of 70 participants during interactions with embodied conversational agents, comparing teleportation to natural walking. Our findings revealed that participants maintained closer proximity from the agents during teleportation. Female participants kept greater distances from the agents than male participants, and natural walking was associated with higher agency and body ownership, though co-presence remained unchanged. We propose that differences in spatial perception and spatial cognitive load contribute to reduced interpersonal distance with teleportation. These findings emphasise that proximity should be a key consideration when selecting locomotion methods in social VR, highlighting the need for further research on how locomotion impacts spatial perception and social dynamics in virtual environments.

We present an approach for training "agile" character control policies, able to produce a wide variety of motor skills from a single reference motion cycle. Our technique builds off of generative adversarial imitation learning (GAIL), with a key novelty of our approach being to provide modification to the observation map in order to improve agility and robustness. Namely, to support more agile behavior, we adjust the value measurements of the training discriminator through relative features - hence the name ReGAIL. Our state observations include both task relevant relative velocities and poses, as well as relative goal deviation information. In addition, to increase robustness of the resulting gaits, servo gains and damping values are included as part of the policy action to let the controller learn how to best combine tension and relaxation during motion. From a policy informed by a single reference motion, our resulting agent is able to maneuver as needed, at runtime, from walking forward to walking backward or sideways, turning and stepping nimbly. We demonstrate our approach for a humanoid and a quadruped, on both flat and sloped terrains, as well as provide ablation studies to validate the design choices of our framework.

We present a framework for the interactive simulation of fluids coupled with rigid bodies that targets heterogeneous distributed computing architectures. Specifically, our proposed approach is well-suited for computer graphics applications that combine servers with large compute capactiy with low-end devices. In this setting, a global large-scale fluid simulation is performed on servers using high-end compute hardware, and local refinement of fluid and rigid body coupling is performed on a client with limited compute resources, such as a tablet or smartphone. We demonstrate the effectiveness of our framework to simulate large and complex scenes involving wind, ocean, and dynamic objects, all while providing plausible interactions through fluid-rigid coupling.

We present an approach for generating facial animation that combines video and audio input data in real time for low-end devices through deep learning. Our method produces control signals from audiovisual inputs separately, and mixes them to animate a character rig. The architecture relies on two specialized networks that are trained on a combination of synthetic and real world data and are highly engineered to be efficient in order to support quality avatar faces even on low-end devices. In addition, the system supports several levels of detail that degrade gracefully for additional scaling and efficiency. We showcase how user testing has been employed to improve performance and a comparison with state of the art.

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control. Code is available at https://motion-lab.github.io/CompositeMotion .

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available at https://motion-lab.github.io/AdaptNet .

Cartoon effects described in animation principles are key to adding fluidity and style to animated characters. This paper extends the existing framework of Velocity Skinning to use skeletal acceleration, in addition to velocity, for cartoon-style effects on rigged characters. This Acceleration Skinning is able to produce a variety of cartoon effects from highly efficient closed-form deformers while remaining compatible with standard production pipelines for rigged characters. The paper showcases the introduction of the framework along with providing applications through three new deformers. Specifically, a followthrough effect is obtained from the combination of skeletal acceleration and velocity. Also, centrifugal stretch and centrifugal lift effects are introduced using rotational acceleration to model radial stretching and lifting. The paper also explores the application of effect-specific time filtering when combining deformations together allowing for more stylization and artist control over the results.

Locomotion in humans and animals is highly coordinated, with many joints moving together. Learning similar coordinated locomotion in articulated virtual characters, in the absence of reference motion data, is a challenging task due to the high number of degrees of freedom and the redundancy that comes with it. In this paper, we present a method for learning locomotion for virtual characters in a low dimensional latent space which defines how different joints move together. We introduce a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitations, resulting in a corpus of motion data from which a manifold latent space is extracted. Dimensions of the extracted manifold define a wide variety of synergies pertaining to the character and, through reinforcement learning, we train the character to learn locomotion in the latent space by selecting a small set of appropriate latent dimensions, along with learning the corresponding policy.

Abstract Secondary animation effects are essential for liveliness. We propose a simple, real‐time solution for adding them on top of standard skinning, enabling artist‐driven stylization of skeletal motion. Our method takes a standard skeleton animation as input, along with a skin mesh and rig weights. It then derives per‐vertex deformations from the different linear and angular velocities along the skeletal hierarchy. We highlight two specific applications of this general framework, namely the cartoon‐like “squashy” and “floppy” effects, achieved from specific combinations of velocity terms. As our results show, combining these effects enables to mimic, enhance and stylize physical‐looking behaviours within a standard animation pipeline, for arbitrary skinned characters. Interactive on CPU, our method allows for GPU implementation, yielding real‐time performances even on large meshes. Animator control is supported through a simple interface toolkit, enabling to refine the desired type and magnitude of deformation at relevant vertices by simply painting weights. The resulting rigged character automatically responds to new skeletal animation, without further input.

We propose an approach for motor skill learning of highly articulated characters based on the systematic exploration of low-dimensional joint coactivation spaces. Through analyzing human motion, we first show that the dimensionality of many motion tasks is much smaller than the full degrees of freedom (DOFs) of the character. Indeed, joint motion appears organized across DOFs, with multiple joints moving together and working in synchrony. We exploit such redundancy for character control by extracting task-specific joint coactivations from human recorded motion, capturing synchronized patterns of simultaneous joint movements that effectively reduce the control space across DOFs. By learning how to excite such coactivations using deep reinforcement learning, we are able to train humanlike controllers using only a small number of dimensions. We demonstrate our approach on a range of motor tasks and show its flexibility against a variety of reward functions, from minimalistic rewards that simply follow the center-of-mass of a reference trajectory to carefully shaped ones that fully track reference characters. In all cases, by learning a 10-dimensional controller on a full 28 DOF character, we reproduce high-fidelity locomotion even in the presence of sparse reward functions.

This paper proposes a real-time approach to animate a character walking in different environments for a video game setting. The technique combines a physics-based model with procedural animation and motion editing. Highlighting environmental interaction in fluids, the paper leverages simplified drag forces to drive realistic changes from existing locomotion data. To demonstrate generalizability of the effort, we also generate forces from impulse felt from interactions in the environment.

We present a novel extended partitioned method for two-way solid-fluid coupling, where the fluid and solid solvers are treated as black boxes with limited exposed interfaces, facilitating modularity and code reusability. Our method achieves improved stability and extended range of applicability over standard partitioned approaches through three techniques. First, we couple the black-box solvers through a small, reduced-order monolithic system, which is constructed on the fly from input/output pairs generated by the solid and fluid solvers. Second, we use a conservative, impulse-based interaction term to couple the solid and fluid rather than typical pressure-based forces. We show that both of these techniques significantly improve stability and reduce the number of iterations needed for convergence. Finally, we propose a novel boundary pressure projection method that allows for the partitioned simulation of a fully enclosed fluid coupled to a dynamic solid, a scenario that has been problematic for partitioned methods. We demonstrate the benefits of our extended partitioned method by coupling Eulerian fluid solvers for smoke and water to Lagrangian solid solvers for volumetric and thin deformable and rigid objects in a variety of challenging scenarios. We further demonstrate our method by coupling a Lagrangian SPH fluid solver to a rigid body solver.

We present an immersive Virtural Reality (VR) experience developed through a unique combination of technologies including an actuated hardware rig; a physics model with a responsive control routine; and an interactive 3D gamelike experience. Specifically, this paper introduces a physics-based communication framework that allows force-driven interaction to be conveyed to a user through a physics-based proxy. Because the framework is generic and extendable, the application supports a variety of interaction modes, constrained by the limitations of the physical full-body haptic rig. To showcase the technology, we highlight the experience of riding locomoting robots and vehicles placed in an immersive VR setting.

Abstract Physically based characters have not yet received wide adoption in the entertainment industry because control remains both difficult and unreliable. Even with the incorporation of motion capture for reference, which adds believability, characters fail to be convincing in their appearance when the control is not robust. To address these issues, we propose a simple Jacobian transpose torque controller that employs virtual actuators to create a fast and reasonable tracking system for motion capture. We combine this controller with a novel approach we call the topple‐free foot strategy which conservatively applies artificial torques to the standing foot to produce a character that is capable of performing with arbitrary robustness. The system is both easy to implement and straightforward for the animator to adjust to the desired robustness, by considering the trade‐off between physical realism and stability. We showcase the benefit of our system with a wide variety of example simulations, including energetic motions with multiple support contact changes, such as capoeira, as well as an extension that highlights the approach coupled with a Simbicon controlled walker. With this work, we aim to advance the state‐of‐the‐art in the practical design for physically based characters that can employ unaltered reference motion (e.g. motion capture data) and directly adapt it to a simulated environment without the need for optimization or inverse dynamics.

American Sign Language (ASL) fingerspelling is the act of spelling a word letter-by-letter when a specific sign does not exist to represent it. Synthesizing intelligible ASL, which includes fingerspelling as an integral part, is important to create signing virtual characters for training and communicating in virtual environments or further applications. The rhythm and speed of fingerspelling play a large role in how well fingerspelling is understood. Using motion capture technologies, we record fingerspelling and analyze timing information about letters in the words. Our goal is to identify fingerspelling timing information and use it to create fingerspelling animations that are natural and understandable.

Abstract The human hand is a complex biological system able to perform numerous tasks with impressive accuracy and dexterity. Gestures furthermore play an important role in our daily interactions, and humans are particularly skilled at perceiving and interpreting detailed signals in communications. Creating believable hand motions for virtual characters is an important and challenging task. Many new methods have been proposed in the Computer Graphics community within the last years, and significant progress has been made towards creating convincing, detailed hand and finger motions. This state of the art report presents a review of the research in the area of hand and finger modeling and animation. Starting with the biological structure of the hand and its implications for how the hand moves, we discuss current methods in motion capturing hands, data‐driven and physics‐based algorithms to synthesize their motions, and techniques to make the appearance of the hand model surface more realistic. We then focus on areas in which detailed hand motions are crucial such as manipulation and communication. Our report concludes by describing emerging trends and applications for virtual hand animation.

This paper proposes a physics based model to simulate a reactive camera that is capable of both high-quality tracking of moving target objects and producing plausible response interactively to a variety of game scenarios. The virtual physical rig consists of a motorized pan-tilt head that is controlled to meet desired target look-at directions as well as an active suspension system that stabilizes the camera assembly against disturbances. To showcase its differences with other camera systems, we contrast our physically based technique with other direct (kinematic) computed methods from industry standard techniques.

This paper proposes a physics-based framework to control rolling, flipping and other behaviors with significant rotational components. The proposed technique is a general approach for guiding coordinated action that can be layered over existing control architectures through the purposeful regulation of specific whole-body features. Namely, we apply control for rotation through the specification and execution of specific desired `rotation indices' for whole-body orientation, angular velocity and angular momentum control and highlight the use of the angular excursion as a means for whole-body rotation control. We account for the stylistic components of behaviors through reference posture control. The novelty of the described work includes control over behaviors with considerable rotational components, both on the ground and in the air as well as a number of characteristics useful for general control, such as flight planning with inertia modeling, compliant posture tracking, and contact control planning.

This paper introduces a method for producing high quality hand motion using a small number of markers. The proposed "handover" animation technique constructs joint angle trajectories with the help of a reference database. Utilizing principle component analysis (PCA) applied to the database, the system automatically determines the sparse marker set to record. Further, to produce hand animation, PCA is used along with a locally weighted regression (LWR) model to reconstruct joint angles. The resulting animation is a full-resolution hand which reflects the original motion without the need for capturing a full marker set. Comparing the technique to other methods reveals improvement over the state of the art in terms of the marker set selection. In addition, the results highlight the ability to generalize the motion synthesized, both by extending the use of a single reference database to new motions, and from distinct reference datasets, over a variety of freehand motions.

Control for physically based characters presents a challenging task because it requires not only the management of the functional aspects that lead to the successful completion of the desired task, but also the resulting movement must be visually appealing and meet the quality requirements of the application. Crafting controllers to generate desirable behaviors is difficult because the specification of the final outcome is indirect and often at odds with the functional control of the task. This paper presents a method which exploits the natural modal vibrations of a physically based character in order to provide a palette of basis coordinations that animators can use to assemble their desired motion. A visual user interface allows an animator to guide the final outcome by selecting and inhibiting the use of specific modes. Then, an optimization routine applies the user-chosen modes in the tuning of parameters for a fixed locomotion control structure. The result is an animation system that is easy for an animator to drive and is able to produce a wide variety of locomotion styles for varying character morphologies.

Most game interfaces today are largely symbolic, translating simplified input such as keystrokes into the choreography of full-body character movement. In this paper, we describe a system that directly uses human motion performance to provide a radically different, and much more expressive interface for controlling virtual characters. Our system takes a data feed from a motion capture system as input, and in real-time translates the performance into corresponding actions in a virtual world. The difficulty with such an approach arises from the need to manage the discrepancy between the real and virtual world, leading to two important subproblems 1) recognizing the user's intention, and 2) simulating the appropriate action based on the intention and virtual context. We solve this issue by first enabling the virtual world's designer to specify possible activities in terms of prominent features of the world along with associated motion clips depicting interactions. We then integrate the prerecorded motions with online performance and dynamic simulation to synthesize seamless interaction of the virtual character in a simulated virtual world. The result is a flexible interface through which a user can make freeform control choices while the resulting character motion maintains both physical realism and the user's personal style.

We demonstrate a real-time simulation system capable of automatically balancing a standing character, while at the same time tracking a reference motion and responding to external perturbations. The system is general to non-human morphologies and results in natural balancing motions employing the entire body (for example, wind-milling). Our novel balance routine seeks to control the linear and angular momenta of the character. We demonstrate how momentum is related to the center of mass and center of pressure of the character and derive control rules to change these centers for balance. The desired momentum changes are reconciled with the objective of tracking the reference motion through an optimization routine which produces target joint accelerations. A hybrid inverse/forward dynamics algorithm determines joint torques based on these joint accelerations and the ground reaction forces. Finally, the joint torques are applied to the free-standing character simulation. We demonstrate results for following both motion capture and keyframe data as well as both human and non-human morphologies in presence of a variety of conditions and disturbances.

We present a novel technique for generating animation of laughter for a character. Our approach utilizes an anatomically inspired, physics-based model of a human torso that includes a mix of rigid-body and deformable components and is driven by Hill-type muscles. We propose a hierarchical control method which synthesizes laughter from a simple set of input signals. In addition, we present a method for automatically creating an animation from a soundtrack of an individual laughing. We show examples of laugh animations generated by hand-selected input parameters and by our audio-driven optimization approach. We also include results for other behaviors, such as coughing and a sneeze, created using the same model. These animations demonstrate the range of possible motions that can be generated using the proposed system. We compare our technique with both data-driven and procedural animations of laughter.

We present a psychology-inspired approach for generating a character' s anticipation of and response to an impending head or upper body impact. Protective anticipatory movement is built upon several actions that have been identified in the psychology literature as response mechanisms in monkeys and in humans. These actions are parameterized by a model of the approaching object (the threat) and are defined as procedural rules. We present a hybrid forward and inverse kinematic blending technique to guide the character to the pose that results from these rules while maintaining properties of a balanced posture as well as characteristics of the behavior just prior to the interaction. In our case, these characteristics are determined by a motion capture sequence. We combine our anticipation model with a physically-based dynamic response to produce animations where a character anticipates an impact before collision and reacts to the contact, physically, after the collision. We present a variety of examples including threats that vary in approach direction, size and speed.

Time series motifs are approximately repeated patterns foundwithin the data. Such motifs have utility for many data mining algorithms, including rule-discovery,novelty-detection, summarization and clustering. Since the formalization of the problem and the introduction of efficient linear time algorithms, motif discovery has been successfully applied tomany domains, including medicine, motion capture, robotics and meteorology.In this work we show that most previous applications of time series motifs have been severely limited by the definition's brittleness to even slight changes of uniform scaling, the speed at which the patterns develop. We introduce a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and show that it produces objectively superior results in several important domains. Apart from being more general than all other motifdiscovery algorithms, a further contribution of our work isthat it is simpler than previous approaches, in particular we have drastically reduced the number of parameters that need to be specified.

Motion graphs have gained popularity in recent years as a means for re-using motion capture data by connecting previously unrelated segments of a recorded library. Current techniques for controlling movement of a character via motion graphs have largely focused on path planning which is difficult due to the density of connections found on the graph. We introduce "state-annotated motion graphs," a novel technique which allows high-level control of character behavior by using a dual representation consisting of both a motion graph and a behavior state machine. This special motion graph is generated from labeled data and then bound to a finite state machine with similar labels. At run-time, character behavior is simply controlled by switching states. We show that it is possible to generate rich, controllable motion without the need for deep planning. We demonstrate that, when applied to an interactive fighting testbed, simple state-switching controllers may be coded intuitively to create various effects.

Automatically generated anticipation is a largely overlooked component of response in character motion for computer animation. We present an approach for generating anticipation to unexpected interactions with examples taken from human motion capture data. Our system generates animation by quickly selecting an anticipatory action using a Support Vector Machine (SVM) which is trained offline to distinguish the characteristics of a given scenario according to a metric that assesses predicted damage and energy expenditure for the character. We show our results for a character that can anticipate by blocking or dodging a threat coming from a variety of locations and targeting any part of the body, from head to toe.

Human motion capture embeds rich detail and style which is difficult to generate with competing animation synthesis technologies. However, such recorded data requires principled means for creating responses in unpredicted situations, for example reactions immediately following impact. This paper introduces a novel technique for incorporating unexpected impacts into a motion capture-driven animation system through the combination of a physical simulation which responds to contact forces and a specialized search routine which determines the best plausible re-entry into motion library playback following the impact. Using an actuated dynamic model, our system generates a physics-based response while connecting motion capture segments. Our method allows characters to respond to unexpected changes in the environment based on the specific dynamic effects of a given contact while also taking advantage of the realistic movement made available through motion capture. We show the results of our system under various conditions and with varying responses using martial arts motion capture as a testbed.