Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable Diffusion model. We introduce a depth branch into U-Net for efficient and high quality multi-view, cross-domain generation and incorporate epipolar attention into the latent-to-pixel decoder for pixel-level multi-view consistency. By back-projecting the generated depth pixels into 3D space, we create a structured 3D representation that can be either rendered via Gaussian splatting or extracted to high-quality meshes, thereby leveraging additional novel view synthesis loss to further improve our performance. Extensive experiments demonstrate that our method surpasses existing baselines in geometry and texture quality while achieving significantly faster generation time.

In theory, diffusion curves promise complex color gradations for infinite-resolution vector graphics. In practice, existing realizations suffer from poor scaling, discretization artifacts, or insufficient support for rich boundary conditions. Previous applications of the boundary element method to diffusion curves have relied on polygonal approximations, which either forfeit the high-order smoothness of Bézier curves, or, when the polygonal approximation is extremely detailed, result in large and costly systems of equations that must be solved. In this paper, we utilize the boundary integral equation method to accurately and efficiently solve the underlying partial differential equation. Given a desired resolution and viewport, we then interpolate this solution and use the boundary element method to render it. We couple this hybrid approach with the fast multipole method on a non-uniform quadtree for efficient computation. Furthermore, we introduce an adaptive strategy to enable truly scalable infinite-resolution diffusion curves.

Abstract The acquisition of highly detailed static 3D scan data for people in clothing is becoming widely available. Since 3D scan data is given as a single mesh without semantic separation, in order to animate the data, it is necessary to model shape and deformation behaviour of individual body and garment parts. This paper presents a new method for generating simulation‐ready garment models from 3D static scan data of clothed humans. A key contribution of our method is a novel approach to segmenting garments by finding optimal boundaries between the skin and garment. Our boundary‐based garment segmentation method allows for stable and smooth separation of garments by using an implicit representation of the boundary and its optimization strategy. In addition, we present a novel framework to construct a 2D pattern from the segmented garment and place it around the body for a draping simulation. The effectiveness of our method is validated by generating garment patterns for a number of scan data.

We present a new approach for modelling musculoskeletal anatomy. Unlike previous methods, we do not model individual muscle shapes as geometric primitives (polygonal meshes, NURBS etc.). Instead, we adopt a volumetric segmentation approach where every point in our volume is assigned to a muscle, fat, or bone tissue. We provide an interactive modelling tool where the user controls the segmentation via muscle curves and we visualize the muscle shapes using volumetric rendering. Muscle curves enable intuitive yet powerful control over the muscle shapes. This representation allows us to automatically handle intersections between different tissues (muscle-muscle, muscle-bone, and muscle-skin) during the modelling and automates computation of muscle fiber fields. We further introduce a novel algorithm for converting the volumetric muscle representation into tetrahedral or surface geometry for use in downstream tasks. Additionally, we introduce an interactive skeleton authoring tool that allows the users to create skeletal anatomy starting from only a skin mesh using a library of bone parts.

We present a novel approach to enrich arbitrary rig animations with elastodynamic secondary effects. Unlike previous methods which pit rig displacements and physical forces as adversaries against each other, we advocate that physics should complement artists' intentions. We propose optimizing for elastodynamic displacements in the subspace orthogonal to displacements that can be created by the rig. This ensures that the additional dynamic motions do not undo the rig animation. The complementary space is high-dimensional, algebraically constructed without manual oversight, and capable of rich high-frequency dynamics. Unlike prior tracking methods, we do not require extra painted weights, segmentation into fixed and free regions or tracking clusters. Our method is agnostic to the physical model and plugs into non-linear FEM simulations, geometric as-rigid-as-possible energies, or mass-spring models. Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.

Despite the recent advances in automatic methods for computing skinning weights, manual intervention is still indispensable to produce high-quality character deformation. However, current modeling software does not provide efficient tools for the manual definition of skinning weights. Widely used paint-based interfaces give users high degrees of freedom, but at the expense of significant efforts and time. This article presents a novel interface for editing skinning weights based on splines, which represent the isolines of skinning weights on a mesh. When a user drags a small number of spline anchor points, our method updates the shape of the isolines and smoothly interpolates or propagates the weights while respecting the given iso-value on the spline. We introduce several techniques to enable the interface to run in real-time and propose a particular combination of functions that generates appropriate skinning weight over the surface. Users can create skinning weights from scratch by using our method. In addition, we present the spline and the gradient fitting methods that closely approximate initial given weights, so that a user can modify the weights with our spline interface. We show the effectiveness of our spline-based interface through a number of test cases.

Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

Abstract 3D objects of the same kind often have different topologies, and finding correspondence between them is important for operations such as morphing, attribute transfer, and shape matching. This paper presents a novel method to find the surface correspondence between topologically different surfaces. The method is characterized by deforming the source polygonal mesh to match the target mesh by using the intermediate implicit surfaces, and by performing a topological surgery at the appropriate locations on the mesh. In particular, we propose a mathematically well‐defined way to detect the topology change of surface by finding the non‐degenerate saddle points of the velocity fields that tracks implicit surfaces. We show the effectiveness and possible applications of the proposed method through several experiments.

In augmented reality (AR) applications, a virtual avatar serves as a useful medium to represent a human in a different place. This paper deals with the problem of retargeting a human motion to an avatar. In particular, we present a novel method that retargets a human motion with respect to an object to that of an avatar with respect to a different object of a similar shape. To achieve this, we developed a spatial map that defines the correspondences between any points in the 3D spaces around the respective objects. The key advantage of the spatial map is that it identifies the desired locations of the avatar's body parts for any input motion of a human. Once the spatial map is created offline, the motion retargeting can be performed in real-time. The retargeted motion preserves important features of the original motion such as the human pose and the spatial relation with the object. We report the results of a number of experiments that demonstrate the effectiveness of the proposed method.

Rigging is a core element in the process of bringing a 3D character to life. The rig defines and delimits the motions of the character and provides an interface for an animator with which to interact with the 3D character. The quality of the rig has a key impact on the expressiveness of the character. Creating a usable, rich, production ready rig is a laborious task requiring direct intervention by a trained professional because the goal is difficult to achieve with fully automatic methods. We propose a semi-automatic rigging editing framework which eases the need for manual intervention while maintaining an important degree of control over the final rig. Starting by automatically generated base rig, we provide interactive operations which efficiently configure the skeleton structure and mesh skinning.