Abstract For decades, real‐time skinning has been the cornerstone of character animation in visual effects and games. Despite its importance, the creation of animatable digital assets remains a labor‐intensive manual process. Existing automated tools frequently struggle with intricate geometries, often necessitating significant manual refinement to reach production standards. We present a robust, fully automated method for generating high‐quality skinning weights from a standard mesh and skeleton in a canonical A‐ or T‐pose. Unlike traditional approaches, our framework offers direct sparsity controls to limit bone influences per vertex – a critical requirement for maintaining performance in large‐scale mobile environments. Furthermore, we address the challenge of Level‐of‐Detail (LoD) management by optimizing weights within a continuous spatial volume rather than on discrete vertices. This allows a single optimization pass to be applied seamlessly across multiple asset resolutions and variations. Central to our approach is a novel parameterized family of functions, we call SkinCells. We demonstrate that our method consistently produces stable, high‐quality results even in complex scenarios where standard biharmonic weight computations fail.

Abstract Real‐time hair simulation is a vital component in creating believable virtual avatars, as it provides a sense of immersion and authenticity. The dynamic behavior of hair, such as bouncing or swaying in response to character movements like jumping or walking, plays a significant role in enhancing the overall realism and engagement of virtual experiences. Current methods for simulating hair have been constrained by two primary approaches: highly optimized physics‐based systems and neural methods. However, state‐of‐the‐art neural techniques have been limited to quasi‐static solutions, failing to capture the dynamic behavior of hair. This paper introduces a novel neural method that breaks through these limitations, achieving efficient and stable dynamic hair simulation while outperforming existing approaches. We propose a fully self‐supervised method which can be trained without any manual intervention or artist generated training data allowing the method to be integrated with hair reconstruction methods to enable automatic end‐to‐end methods for avatar reconstruction. Our approach harnesses the power of compact, memory‐efficient neural networks to simulate hair at the strand level, allowing for the simulation of diverse hairstyles without excessive computational resources or memory requirements. We validate the effectiveness of our method through a variety of hairstyle examples, showcasing its potential for real‐world applications.

Realistic hair motion is crucial for high-quality avatars, but it is often limited by the computational resources available for real-time applications. To address this challenge, we propose a novel neural approach to predict physically plausible hair deformations that generalizes to various body poses, shapes, and hairstyles. Our model is trained using a self-supervised loss, eliminating the need for expensive data generation and storage. We demonstrate our method’s effectiveness through numerous results across a wide range of pose and shape variations, showcasing its robust generalization capabilities and temporally smooth results. Our approach is highly suitable for real-time applications with an inference time of only a few milliseconds on consumer hardware and its ability to scale to predicting the drape of 1000 grooms in 0.3 seconds.

In recent years, reconstruction methods have been developed that can recover strand-level hair geometry from images. However, these methods recover a vast number of individual hair strands that are difficult to edit and simulate. Many methods also rely on neural priors to infer non-visible inner hair, which can result in poor inner hair structure for complex hairstyles, such as curly hair. We propose an inverse hair grooming pipeline that transforms the imperfect 3D strands from these reconstruction methods into procedural hair grooms that consist of a small set of guide strands and hair grooming operators, inspired by pipelines used by artists in popular 3D modeling tools such as Blender and Houdini. We take a probabilistic view of these hair grooms and design various optimization strategies and loss functions to optimize for the guide strands and operator parameters. Due to the proceduralism, our resulting grooms can naturally represent challenging hairstyles, have structurally sound inner hair, and are easily editable.

One of the pleasures of interacting using avatars in VR is being able to play a character very different to yourself. As the scale of characters change relative to a user, there is a need to retarget user motions onto the character, generally maintaining either the user's pose or the position of their wrists and ankles. This retargeting can impact both the functional and social information conveyed by the avatar. Focused on 3rd-person (observed) avatars, this paper presents three studies on these varied aspects of communication. It establishes a baseline for near-field avatar pointing, showing an accuracy of about 5cm. This can be maintained using positional hand constraints, but increases if the user's pose is directly transferred to the character. It is possible to maintain this accuracy with a Semantic Inverse Kinematics formulation that brings the avatar closer to the user's actual pose, but compensates by adjusting the finger pointing direction. Similar results are shown for conveying spatial information, namely object size. The choice of pose or position based retargeting leads to a small change in the perception of avatar personality, indicating an impact on social communication. This effect was not observed in a task where the users' cognitive load was otherwise high, so may be task dependent. It could also become more pronounced for more extreme proportion changes.

Virtual Reality (VR) affords great freedom in how one represents themselves in virtual interactions through the selection of different avatars. However, it remains unclear which avatar should be chosen for a given social scenario. Social interaction often relies on the establishment of trust. Are people more likely to trust you if you select a highly realistic avatar or is there flexibility in representation? This work presents a study exploring this question using a high stakes medical scenario. Participants meet three different doctors with three different style levels: realistic, caricatured, and an in-between “Mid” level. Trust ratings are largely consistent across the style levels, but participants were more likely to select doctors with the “Mid” level of stylization for a second opinion. There is a clear preference against one of the three doctor identities, with evidence that this may be related to movement features.

We propose a simple algorithm for automatic transfer of facial expressions, from videos to a 3D character, as well as between distinct 3D characters through their rendered animations. Our method begins by learning a common, semantically-consistent latent representation for the different input image domains using an unsupervised image-to-image translation model. It subsequently learns, in a supervised manner, a linear mapping from the character images' encoded representation to the animation coefficients. At inference time, given the source domain (i.e., actor footage), it regresses the corresponding animation coefficients for the target character. Expressions are automatically remapped between the source and target identities despite differences in physiognomy. We show how our technique can be used in the context of markerless motion capture with controlled lighting conditions, for one actor and for multiple actors. Additionally, we show how it can be used to automatically transfer facial animation between distinct characters without consistent mesh parameterization and without engineered geometric priors. We compare our method with standard approaches used in production and with recent state-of-the-art models on single camera face tracking.

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