Embodying photorealistic personalized avatars that closely resemble their users has recently become technologically achievable in virtual reality (VR). While previous work highlighted several benefits of personalization, its impact on emotions, a central VR element, remains underexplored. Given their high production cost, assessing the added value of photorealistic personalized avatars is essential before their adoption as a design feature. To address this, we designed and validated (n=302) four virtual environments (VEs) that induce emotions in different quadrants of Russell’s Circumplex Model. Using these VEs, we systematically investigated the impact of avatar type (no avatar, generic avatar, personalized avatar) on perceived emotions and physiological responses (n=51). Generic avatars enhanced valence, arousal, embodiment, presence and selected physiological measures, while personalized avatars further amplified these effects and additionally increased heart rate variability, a marker of self-regulation. Our study demonstrates that photorealistic personalized avatars markedly enhance VR’s capacity to foster emotional engagement.

In immersive applications, reconstructing full-body self-avatar motion remains a challenging task, particularly in the absence of lowerbody tracking devices. A promising approach involves supplementing sparse upper-body motion signals with 3D Cartesian joint positions estimated from external RGB cameras. While effective, such pose estimators are prone to inaccuracies, jitter, and occlusions, which can negatively impact reconstruction quality. In this work, we investigate the robustness of self-avatar animation models to such artifacts and to variations across pose estimators. We evaluate two state-of-the-art models, HMD-Poser and AvatarJLM, and show that integrating RGB-based pose data improves lower-body accuracy over VR-only baselines, even for inaccurate 3D pose. However, model performance degrades when training and testing rely on different pose estimators. This highlights the sensitivity of current approaches to estimator variability and underscores the need for estimator-aware training to ensure robustness in real-world deployment.

Many VR animal embodiment sytsems suffer from poor animation fidelity, typically animating the animal avatars using inverse kinematics. We address this issue, presenting a novel deep-learning method, centred around a shared codebook, for mapping human motion to quadruped motion. Rather than trying to directly bridge the gap from human motion to quadruped motion, a task which has proven difficult, we first use a rule-based retargeter, relying on inverse and forward kinematics, to retarget human motions to an intermediate motion domain in which the motions share the same skeleton as the quadruped. We then use finite scalar quantization to construct a shared latent space, or codebook, between this intermediate domain and the quadruped motion domain. We do this by first pre-defining a finite number of discrete latent codes and then teaching these codes, using unsupervised deep-learning, to represent semantically similar motions in the two domains. We incorporate our real-time human-to-quadruped motion mapping into a VR quadruped embodiment system. The output quadruped animations are natural and realistic, while also preserving the semantics of users’ actions. Moreover, there is a strong synchrony between the input human motions and retargeted quadruped motions, an important factor for inducing a strong sense of VR embodiment.

Embodied personalized avatars are a promising new tool to investigate moral decision-making by transposing the user into the "middle of the action" in moral dilemmas. Here, we tested whether avatar personalization and motor control could impact moral decision-making, physiological reactions and reaction times, as well as embodiment, presence and avatar perception. Seventeen participants, who had their personalized avatars created in a previous study, took part in a range of incongruent (i.e., harmful action led to better overall outcomes) and congruent (i.e., harmful action led to trivial outcomes) moral dilemmas as the drivers of a semi-autonomous car. They embodied four different avatars (counterbalanced - personalized motor control, personalized no motor control, generic motor control, generic no motor control). Overall, participants took a utilitarian approach by performing harmful actions only to maximize outcomes. We found increased physiological arousal (SCRs and heart rate) for personalized avatars compared to generic avatars, and increased SCRs in motor control conditions compared to no motor control. Participants had slower reaction times when they had motor control over their avatars, possibly hinting at more elaborate decision-making processes. Presence was also higher in motor control compared to no motor control conditions. Embodiment ratings were higher for personalized avatars, and generally, personalization and motor control were perceptually positive features. These findings highlight the utility of personalized avatars and open up a range of future research possibilities that could benefit from the affordances of this technology and simulate, more closely than ever, real-life action.

Creating realistic quadruped animations is challenging. Producing realistic animations using methods such as key-framing is time consuming and requires much artistic expertise. Alternatively, motion capture methods have their own challenges (getting the animal into a studio, attaching motion capture markers, and getting the animal to put on the desired performance) and the resulting animation will still most likely require cleaning up. It would be useful if an animator could provide an initial rough animation and in return be given a corresponding high quality realistic one. To this end, we present a deep-learning approach for the automatic enhancement of quadruped animations. Given an initial animation, possibly lacking the subtle details of true quadruped motion and/or containing small errors, our results show that it is possible for a neural network to learn how to add these subtleties and correct errors to produce an enhanced animation while preserving the semantics and context of the initial animation. Our work also has potential uses in other applications, for example, its ability to be used in real-time means it could form part of a quadruped embodiment system.