Human motion synthesis has advanced significantly in domains such as text-to-motion and gesture generation, yet synthesising realistic interaction motion between multiple characters remains relatively underexplored, particularly in dynamic and user-driven environments like virtual reality (VR). Existing approaches rely on constructing sequences across a set number of frames, inherently limiting their usability in real-time and open-ended applications such as virtual reality. In this work, we present a novel latent diffusion-based framework for real-time interaction motion synthesis. Unlike prior models that operate on fixed-length sequences, our system continuously adapts to user behaviour, generating contextually appropriate and temporally coherent response motions. We introduce Interaction Contact Labels, a generalisation of ground contact annotations, to capture physical interdependencies between interacting characters and integrate them into the training objective to improve realism. Our approach enables open-ended, responsive character interactions suitable for live applications in VR, AR, and robotics. Further to this, we demonstrate a real-time implementation of the proposed method, which utilises CycleGANs to combine webcam data with high-quality motion capture data, allowing for easy end-user set-up.
Smearing is an essential effect to expressively convey motion in stylized animations. In this paper, we extend the method of Basset et al. [2024] to better emphasize the main motion's trajectory of an object when generating elongated in-betweens, i.e., when stretching a 3D object along its trajectory to cover adjacent frames. This limits visual artifacts such as intersections that typically occur when trajectories self-overlap due to local rotations or abrupt changes of direction (trajectories with high curvatures or even discontinuities at contacts). We address these cases with minor computational and memory overheads, and offer enhanced impact expressiveness by combining smear and squash-and-stretch effects at collisions.
Recent technological innovations, especially in extended reality (XR) and artificial intelligence (AI), redefine storytelling approaches. These innovations are expanding creative possibilities for filmmakers while transforming how audiences engage with and experience films and other entertainment products. Despite technological advancements, the pre-production phase depends primarily on traditional planning and visualization methods. This research proposes a novel paradigm to create storyboards in XR, leveraging the capabilities of object-detection and pose-estimation systems to benefit the storyboarding phase. An application has been designed and developed to create 3D storyboards on a real scale within a physical environment and all its furniture. Wearing a head-mounted display for XR, users can move in the physical space, enrich it with virtual elements and characters, and frame the environment to obtain storyboard panels that mix real and virtual elements. The proposed system has been tested to assess its usability, and initial findings indicate that users have appreciated this application.
Haptic technology has the potential to substantially support users in mastering complex activities and improving their performance. This paper presents a haptic assistance system for targeting in virtual reality (VR), designed to enhance accuracy and user confidence during aiming tasks by enabling users to sense proximity to and guiding their motion toward a target. To evaluate its effectiveness, we conducted a user study (N = 28) using a custom VR archery simulation, assessing both quantitative performance metrics and qualitative user feedback. With haptic guidance, participants achieved significantly higher targeting accuracy and reported increased spatial awareness, although some also noted a reduced sense of agency. These results suggest that, while haptic assistance offers clear benefits, careful design is essential to avoid overreliance or interference with user autonomy in contexts such as accessibility, gaming, and broader human-computer interaction.
Position-based Dynamics (PBD) algorithms are widely used for simulating deformable models due to their efficiency, robustness and simplicity. However, PBD methods often suffer from the uncontrollable dissipation which leads to highly stiff visual results. Although recent exponential-based frameworks offer practical solutions to this shortcoming, those methods do not provide specific material properties, such as Young's modulus and Poisson's ratio, and implicit volume conservation. To overcome these limitations, we propose to reformulate the potential energy density functions of constitutive materials with an exponential strain tensor for position-based simulation of deformable objects. Our proposed method redefines the well-known potential energy density functions of the linear, corotational, StVK elastic materials and continuous cloth with the exponential strain tensor by conserving their theoretical fundamentals. Therefore, the simulated models provide more vivid visual results against the unexpected numerical dissipation. We compare our method with many well-known methods and demonstrate the advantages. Our method is easy to implement and fits to any existing PBD framework effortlessly.
