ABSTRACT Creating realistic 3D human hair models typically requires substantial manual effort and expertise. To reduce this burden, many methods have been proposed for automatic hair modeling from images. In particular, multi‐view reconstruction approaches based on deep learning or optimization can produce high‐quality results, but they often require specialized capture setups, making them difficult for general users. Methods that use only a single image have also been explored for convenience; however, sketch‐based hair modeling–despite the popularity of sketch input for other 3D objects–remains relatively under‐studied. In this paper, we propose a method for reconstructing diverse hairstyles from sketches. Building on a high‐performance single‐image hair reconstruction pipeline, we extend its input modality to sketches. Given a sketch image and a hair‐region image, our method generates two intermediate representations: A strand map that encodes hair flow, computed by solving a diffusion equation, and a depth map estimated using ControlNet. We then reconstruct a 3D hair model from these maps using the existing reconstruction procedure. Experiments with multiple sketch types demonstrate that our approach can reproduce hair geometry consistent with the input sketches.

ABSTRACT This paper presents a control simulation that represents user‐desired shapes using multiple connected soap bubbles. A previous method attempted to control a single soap bubble using external forces. However, due to the strong surface tension making spherical babbles, elongated shapes could not be achieved. To address this issue, this paper aims to develop a control simulation that achieves diverse soap bubble shapes by dividing the target shape into connected soap bubbles. In our approach, we first generate an initial soap bubble configuration composed of multiple bubbles to represent the target shape. Then, by applying external forces to each bubble, we simulate the bubbles to maintain their shape along the target form. We use an implicit‐function‐like representation for the connected soap bubbles and develop a new polygonizer that makes shapes including the internal faces of bubbles. By demonstrating examples with various target shapes such as objects and text, we show the effectiveness of our proposed control method.

ABSTRACT Smoke simulation is a crucial element in entertainment applications, such as movies and video games. In particular, the required smoke texture varies depending on the scene, ranging from smooth textures for cigarette smoke to highly irregular ones for explosions. The texture of smoke is primarily influenced by light sources, scattering properties, and turbulence components. Light sources and scattering properties affect the appearance of smoke during rendering, influencing its color, brightness, and density. Turbulence components significantly influence the shape of smoke. While many methods have been proposed for generating turbulence components, they all require manual adjustment of turbulence parameters, which is both time‐consuming and labor‐intensive. To address this issue, we propose a method for easily generating the desired turbulence by estimating turbulence parameters from images. In our approach, users input images including the desired turbulence components, and optimal parameters representing those components are automatically estimated. This reduces the time and effort required for parameter adjustment, allowing the desired turbulence components to be represented more efficiently.

The dynamic effects of smoke are impressive in illustration design, but it is a troublesome and challenging issue for inexpert users to design smoke effects without domain knowledge of fluid simulations. In this work, we propose DualSmoke, a two-stage global-to-local generation framework for interactive smoke illustration design. In the global stage, the proposed approach utilizes fluid patterns to generate Lagrangian coherent structures from the user’s hand-drawn sketches. In the local stage, detailed flow patterns are obtained from the generated coherent structure. Finally, we apply a guiding force field to the smoke simulator to produce the desired smoke illustration. To construct the training dataset, DualSmoke generates flow patterns using finite-time Lyapunov exponents of the velocity fields. The synthetic sketch data are generated from the flow patterns by skeleton extraction. Our user study verifies that the proposed design interface can provide various smoke illustration designs with good user usability. Our code is available at https://githubcom/shasph/DualSmoke.

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

High-resolution fluid simulations are computationally expensive, so many post-processing methods have been proposed to add turbulent details to low-resolution flows. Guiding methods are one promising approach for adding naturalistic, detailed motions as a post-process, but can be inefficient. Thus, we propose a novel, efficient method that formulates fluid guidance as a minimization problem in stream function space. Input flows are first converted into stream functions, and a high resolution flow is then computed via optimization. The resulting problem sizes are much smaller than previous approaches, resulting in faster computation times. Additionally, our method does not require an expensive pressure projection, but still preserves mass. The method is both easy to implement and easy to control, as the user can control the degree of guiding with a single, intuitive parameter. We demonstrate the effectiveness of our method across various examples.

Generating realistic fluid simulations remains computationally expensive, and animators can expend enormous effort trying to achieve a desired motion. To reduce such costs, several methods have been developed in which high-resolution turbulence is synthesized as a post process. Since global motion can then be obtained using a fast, low-resolution simulation, less effort is needed to create a realistic animation with the desired behavior. While much research has focused on accelerating the low-resolution simulation, the problem controlling the behavior of the turbulent, high-resolution motion has received little attention. In this paper, we show that style transfer methods from image editing can be adapted to transfer the turbulent style of an existing fluid simulation onto a new one. We do this by extending example-based image synthesis methods to handle velocity fields using a combination of patch-based and optimization-based texture synthesis. This approach allows us to take into account the incompressibility condition, which we have found to be a important factor during synthesis. Using our method, a user can easily and intuitively create high-resolution fluid animations that have a desired turbulent motion.

The computational cost for creating realistic fluid animations by numerical simulation is generally expensive. In digital production environments, existing precomputed fluid animations are often reused for different scenes in order to reduce the cost of creating scenes containing fluids. However, applying the same animation to different scenes often produces unacceptable results, so the animation needs to be edited. In order to help animators with the editing process, we develop a novel method for synthesizing the desired fluid animations by combining existing flow data. Our system allows the user to place flows at desired positions and combine them. We do this by interpolating velocities at the boundaries between the flows. The interpolation is formulated as a minimization problem of an energy function, which is designed to take into account the inviscid, incompressible Navier-Stokes equations. Our method focuses on smoke simulations defined on a uniform grid. We demonstrate the potential of our method by showing a set of examples, including a large-scale sandstorm created from a few flow data simulated in a small-scale space.

Abstract Visual simulation of fire plays an important role in many applications, such as movies and computer games. In these applications, artists are often requested to synthesize realistic fire with a particular behavior. To meet such requirement, we present a feedback control method for fire simulations. The user can design the shape of fire by placing a set of control points. Our method generates a force field and automatically adjusts a temperature at a fire source, based on user specified control points. Experimental results show that our method can control the fire shape.

This paper presents a method for controlling simulated explosions by prediction and optimization. Many methods have been proposed for simulating realistic explosion based on numerical fluid analysis. These methods are widely used in applications such as movies and computer games. In these applications, there is often a requirement for the explosion to conform to a specified final shape. The goal of our research is to meet this requirement by controlling the simulation so that the explosion evolves into a target shape specified by the user. The key ideas of our method are optimization of the initial velocities at the explosion source and predictive control of the simulation. Our method optimizes the initial velocities during the preprocessing step. Next, during the simulation, it predicts the future shape from the history of the explosion shape and controls the simulation to minimize the difference between the target shape and the predicted shape.