In order to connect haptic feedback with visual content in treadmill VR exergames for getting more immersive user experiences, we present a novel optimization-based approach to automatically generate terrains for treadmill exergames that can match well with the user-specified treadmill workout profile. In order to validate the effectiveness of our approach, a series of numerical experiments are conducted to investigate the visual effects of the generated terrain, the virtual walking experience of the treadmill exergame, and the comparison between the manual terrain creation approach and the terrain synthesis approach. 

Terrain Synthesis for Treadmill Exergaming in Virtual Reality

Numerical study and optimisation of the boiling of refrigerant in a vertical corrugated tube using vapour phase tracking

Various terrain modelling methods have been proposed for the past decades, providing efficient and often interactive authoring tools. However, they seldom include any notion of style, which is critical for designers in the entertainment industry. We introduce a new generative network method that bridges the gap between automatic terrain synthesis and authoring, providing a versatile set of authoring tools allowing spatialised style. We build upon the StyleGAN2 architecture and extend it with authoring tools. Given an input sketch or existing elevation map, our method generates a terrain with features that can be authored, enhanced, and augmented using interactive brushes and style manipulation tools. The strength of our approach lies in the versatility and interoperability of the different tools. We validate our method quantitatively with drainage calculation against other previous techniques and qualitatively by asking users to follow a prompt or freely create a terrain.

Authoring Terrains with Spatialised Style

Generating heightfield terrains is a necessary precursor to the depiction of computer‐generated natural scenes in a variety of applications. Authoring such terrains is made challenging by the need for interactive feedback, effective user control, and perceptually realistic output encompassing a range of landforms. We address these challenges by developing a terrain‐authoring framework underpinned by an adaptation of diffusion models for conditional image synthesis, trained on real‐world elevation data. This framework supports automated cleaning of the training set; authoring control through style selection and feature sketches; the ability to import and freely edit pre‐existing terrains, and resolution amplification up to the limits of the source data. Our framework improves on previous machine‐learning approaches by: expanding landform variety beyond mountainous terrain to encompass cliffs, canyons, and plains; providing a better balance between terseness and specificity in user control, and improving the fidelity of global terrain structure and perceptual realism. This is demonstrated through drainage simulations and a user study testing the perceived realism for different classes of terrain. The full source code, blender add‐on, and pre‐trained models are available.

Interactive Authoring of Terrain using Diffusion Models

To date, with the rapid advancement of scanning hardware and CAD software, we are facing technically challenging on how to search and find a desired model from a huge shape repository in a fast and accurate way in this bigdata digital era. Sketch-based 3D model retrieval is a flexible and user-friendly approach to tackling the existing challenges. In this paper, we articulate a novel way for model retrieval by means of sketching and building a 3D model retrieval framework based on deep learning. The central idea is to dynamically adjust the distance between the learned features of sketch and model in the encoded latent space through the utility of several deep neural networks. In the pre-processing phase, we convert all models in the shape database from meshes to point clouds because of its lightweight and simplicity. We first utilize two deep neural networks for classification to generate embeddings of both input sketch and point cloud. Then, these embeddings are fed into our clustering deep neural network to dynamically adjust the distance between encodings of the sketch domain and the model domain. The application of the sketch embedding to the retrieval similarity measurement could continue to improve the performance of our framework by re-mapping the distance between encodings from both domains. In order to evaluate the performance of our novel approach, we test our framework on standard datasets and compare it with other state-of-the-art methods. Experimental results have validated the effectiveness, robustness, and accuracy of our novel method.

Novel Sketch-Based 3D Model Retrieval via Cross-domain Feature Clustering and Matching

The appearance styles of natural terrains vary significantly from region to region in real world, and there is a strong need to effectively produce realistic terrain with certain style in computer graphics. In this paper, we advocate a novel neural network approach to the rapid synthesis of multi-style terrains that could directly learn and infer from real terrain data. The key idea is to explicitly devise a conditional generative adversarial network (GAN) which encourages and favors the maximum-distance embedding of acquired styles in the latent space. Towards this functionality, we first collect a dataset that exhibits apparent terrain style diversity in their style attributes. Second, we design multiple discriminators that can distinguish different terrain styles. Third, we employ discriminators to extract terrain features in different spatial scales, so that the developed generator can produce new terrains by fusing the finer-scale and coarser-scale styles. In our experiments, we collect 10 typical terrain datasets from real terrain data that cover a wide range of regions. Our approach successfully generates realistic terrains with global-to-local style control. The experimental results have confirmed our neural network can produce natural terrains with high fidelity, which are user-friendly to style interpolation and style mixing for the terrain authoring task.

Authoring multi-style terrain with global-to-local control

In this article, we propose a novel hybrid framework by combining smoothed particle hydrodynamics and adaptive narrow band fluid implicit particle method (NB‐FLIP) to faithfully model the multiphysical processes involving heat transfer and phase transition, and to precisely simulate the dynamics of condensed droplets moving along intricate objects. We first formulate a governing physical model built upon an improved phase transition model and an augmented on‐surface drop analysis method to achieve realistic condensation effects over intricate hydrophilic/hydrophobic interface. To achieve both high‐fidelity interactions and high‐resolution visual effects, we further develop an adaptive NB‐FLIP solver with octree‐dictated background grid in order to further enhance the performance of our framework. Experimental results have shown that our approach can be used to efficiently and realistically simulate the small‐scale interaction details between condensed drops and complex objects with arbitrary geometry.

An advanced hybrid smoothed particle hydrodynamics–fluid implicit particle method on adaptive grid for condensation simulation

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Novel Sketch-Based 3D Model Retrieval via Cross-domain Feature Clustering and Matching

Authoring multi-style terrain with global-to-local control

An advanced hybrid smoothed particle hydrodynamics–fluid implicit particle method on adaptive grid for condensation simulation