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.

We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such as those generated by text prompts. The generated assets can be directly draped and simulated on human bodies. We leverage the recent progress of image-to-3D diffusion methods to generate 3D garment geometries. However, since these geometries cannot be utilized directly for downstream tasks, we propose to use them as pseudo ground-truth and set up a mesh deformation optimization procedure that deforms a base template mesh to match the generated 3D target. Carefully designed losses allow the base mesh to freely deform towards the desired target, yet preserve mesh quality and topology such that they can be simulated. Finally, we generate high-fidelity texture maps that are globally and locally consistent and faithfully capture the input guidance, allowing us to render the generated 3D assets. With Garment3DGen users can generate the simulation-ready 3D garment of their choice without the need of artist intervention. We present a plethora of quantitative and qualitative

We introduce a novel approach to reconstruct simulation-ready garments with intricate appearance. Despite recent advancements, existing methods often struggle to balance the need for accurate garment reconstruction with the ability to generalize to new poses and body shapes or require large amounts of data to achieve this. In contrast, our method only requires a multi-view capture of a single static frame. We represent garments as hybrid mesh-embedded 3D Gaussian splats, where the Gaussians capture near-field shading and high-frequency details, while the mesh encodes far-field albedo and optimized reflectance parameters. We achieve novel pose generalization by exploiting the mesh from our hybrid approach, enabling physics-based simulation and surface rendering techniques, while also capturing fine details with Gaussians that accurately reconstruct garment details. Our optimized garments can be used for simulating garments on novel poses, and garment relighting. Project page: phys-gaussian-cloth.github.io.

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.

We present a novel approach to shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be generated from the conditional signal. To this end, we train a conditional Large Reconstruction Model (LRM) for masked reconstruction, using multi-view consistent masks rendered from a randomly generated 3D occlusion, and using one clean viewpoint as the conditional signal. During inference, we manually define a 3D region to edit and provide an edited image from a canonical viewpoint to fill that region. We demonstrate that, in just a single forward pass, our method not only preserves the input geometry in the unmasked region through reconstruction capabilities on par with SoTA, but is also expressive enough to perform a variety of mesh edits from a single image guidance that past works struggle with, while being 2-10x faster than the top-performing prior work.

The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization. While physical simulations can produce realistic motions for clothed humans, they require high-quality garment assets with associated physical parameters for cloth simulations. However, manually creating these assets and calibrating their parameters is labor-intensive and requires specialized expertise. Current methods focus on reconstructing geometry, but don't generate complete assets for physics-based applications. To address this gap, we propose DiffAvatar, a novel approach that performs body and garment co-optimization using differentiable simulation. By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body, our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way. Our experiments demonstrate that our approach generates realistic clothing and body shape suitable for downstream applications. We provide additional insights and results on our webpage: people. csail. mit. edu/liyifei/publication/diffavatar.

Virtual garment simulation has become increasingly important with applications in garment design and virtual try-on. However, reproducing garments faithfully remains a cumbersome process. We propose an end-to-end forward pipeline for estimating parameters of shell material models corresponding to real fabrics with minimal input. In contrast to prior work that relies on complex and often expensive capture systems, our method determines yarn model parameters from Young’s moduli determined during standard yarn stretch tests. We use an extended homogenization method to match yarn-level and shell-level hyperelastic energies with respect to a range of surface deformations represented by the first and second fundamental forms, including anisotropic bending. We optimize the parameters of a shell material model involving uncoupled bending and membrane energies. This allows the simulated shell model to exhibit deformation modes motivated by yarn-level physics in real fabrics. Finally, we validate our results with quantitative and visual comparisons against real world fabrics through stretch tests and drape experiments. Using the homogenized parameters, the shell models are capable of capturing the characteristics of underlying yarn patterns and exhibiting distinct behaviors for different yarn materials.

Optimized parallel implementations on GPU or CPU have dramatically enhanced the fidelity, resolution and accuracy of physical simulations and mesh-based algorithms. However, attaining optimal performance requires expert knowledge and might demand complex code and memory layout optimizations. This adds to the fact that physical simulation algorithms require the implementation of derivatives, which can be a tedious and error-prone process. In recent years, researchers and practitioners have investigated the concept of designing systems that allow for a more expressive definition of mesh-based simulation code. These systems leverage domain-specific languages (DSL), automatic differentiation or symbolic computing to enhance readability of implementations without compromising performance. We follow this line of work and propose a symbolic code generation approach tailored to mesh-based computations on parallel devices. Our system extends related work by incorporating collision handling and a data access synchronization approach, enabling rapid sparse matrix assembly.

The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because hair has complex geometry and appearance and exhibits challenging motion. In this paper, we present a two-stage approach that models hair independently of the head to address these challenges in a data-driven manner. The first stage, state compression, learns a low-dimensional latent space of 3D hair states including motion and appearance via a novel autoencoder-as-a-tracker strategy. To better disentangle the hair and head in appearance learning, we employ multi-view hair segmentation masks in combination with a differentiable volumetric renderer. The second stage optimizes a novel hair dynamics model that performs temporal hair transfer based on the discovered latent codes. To enforce higher stability while driving our dynamics model, we employ the 3D point-cloud autoencoder from the compression stage for denoising of the hair state. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without relying on hair observations as a driving signal.††Project page at https://ziyanwl.github.io/neuwigs/.

We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then faithfully reconstructed and rendered, but also plausible material parameters found and simulated. To create VEOs, we built a multi-view capture system that captures objects under the influence of a compressed air stream. Building on recent advances in model-free, dynamic Neural Radiance Fields, we reconstruct the objects and corresponding deformation fields. We propose to use a differentiable, particle-based simulator to use these deformation fields to find representative material parameters, which enable us to run new simulations. To render simulated objects, we devise a method for integrating the simulation results with Neural Radiance Fields. The resulting method is applicable to a wide range of scenarios: it can handle objects composed of inhomogeneous material, with very different shapes, and it can simulate interactions with other virtual objects. We present our results using a newly collected dataset of 12 objects under a variety of force fields, which will be made available upon publication.

Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is composed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on the strand level. To keep the computational effort manageable, we use guide hairs and classic techniques to expand those into a dense hood of hair. 3) To better enforce temporal consistency and generalization ability of our model, we further optimize the 3D scene flow of our representation with multiview optical flow, using volumetric raymarching. Our method can not only create realistic renders of recorded multi-view sequences, but also create renderings for new hair configurations by providing new control signals. We compare our method with existing work on viewpoint synthesis and drivable animation and achieve state-of-the-art results. https://ziyanw1.github.io/hvh.

We propose a novel multi-view camera pipeline for the reconstruction and registration of dynamic clothing. Our proposed method relies on a specifically designed pattern that allows for precise video tracking in each camera view. We triangulate the tracked points and register the cloth surface in a fine-grained geometric resolution and low localization error. Compared to state-of-the-art methods, our registration exhibits stable correspondence, tracking the same points on the deforming cloth surface along the temporal sequence. As an application, we demonstrate how the use of our registration pipeline greatly improves state-of-the-art pose-based drivable cloth models. Furthermore, we propose a novel model, Garment Avatar , for driving cloth from a dense tracking signal which is obtained from two opposing camera views. The method produces realistic reconstructions which are faithful to the actual geometry of the deforming cloth. In this setting, the user wears a garment with our custom pattern which enables our driving model to reconstruct the geometry. Our code and data are available at https://github.com/HalimiOshri/Pattern-Based-Cloth-Registration-and-Sparse-View-Animation. The released data includes our pattern and registered mesh sequences containing four different subjects and 15k frames in total.

Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one of the core aspects of human self-expression - remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving clothing geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for interactive applications. On the other hand, data-driven deep appearance models are capable of efficiently producing realistic appearance, but struggle at synthesizing geometry of highly dynamic clothing and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit modeling of clothing that exhibit both photorealistic appearance learned from real-world data and realistic clothing dynamics. The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry. Our core contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations. We conduct a thorough evaluation of our model and demonstrate diverse animation results on several subjects and different types of clothing. Unlike previous work on photorealistic full-body avatars, our approach can produce much richer dynamics and more realistic deformations even for many examples of loose clothing. We also demonstrate that our formulation naturally allows clothing to be used with avatars of different people while staying fully animatable, thus enabling, for the first time, photorealistic avatars with novel clothing.

Abstract This paper presents a realistic digital oil painting system, specifically targeted at the real‐time performance on highly resource‐constrained portable hardware such as tablets and iPads. To effectively use the limited computing power, we develop an efficient adaptation of the shallow water equations that models all the characteristic properties of oil paint. The pigments are stored in a multi‐layered structure to model the peculiar nature of pigment mixing in oil paint. The user experience ranges from thick shape‐retaining strokes to runny diluted paint that reacts naturally to the gravity set by tablet orientation. Finally, the paint is rendered in real time using a combination of carefully chosen efficient rendering techniques. The virtual lighting adapts to the tablet orientation, or alternatively, the front‐facing camera captures the lighting environment, which leads to a truly immersive user experience. Our proposed features are evaluated via a user study. In our experience, our system enables artists to quickly try out ideas and compositions anywhere when inspiration strikes, in a truly ubiquitous way. They do not need to carry expensive and messy oil paint supplies.