In this paper, we propose a new data-driven framework for 3D hand motion emotion transfer. Specifically, we first capture highquality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and converted to images to facilitate the motion synthesis process and the new dataset will be available to the public. To the best of our knowledge, this is the first public dataset with annotated hand motions. We further formulate the emotion transfer for 3D hand motion as an Image-to-Image translation problem, and it is done by adapting the StarGAN framework. Our new framework is able to synthesize new motions, given target emotion type and an unseen input motion. Experimental results show that our framework can produce high quality and consistent hand motions.
A handful of approaches have been previously proposed to generate procedurally virtual forestry for virtual worlds and computer games, including plant growth models and point distribution methods. However, there has been no evaluation to date which assesses how effective these algorithms are at modelling real-world phenomena. In this paper, we tackle this issue by evaluating three algorithms used in the generation of virtual forests—a randomly uniform point distribution method (control), a plant competition model, and an iterative random point distribution technique. Our results show that a plant competition model generated more believable content when viewed from an aerial perspective. Interestingly, however, we also found that a randomly uniform point distribution method produced forestry which was rated higher in playability and photorealism, when viewed from a first-person perspective. We conclude that the objective of the game designer is important to consider when selecting an algorithm to generate forestry, as the algorithms produce forestry that is perceived differently.
Surface tension has a strong influence on the shape of fluid interfaces. We propose a method to calculate the corresponding forces efficiently. In contrast to several previous approaches, we discriminate to this end between surface and non-surface SPH particles. Our method effectively smooths the fluid interface, minimizing its curvature. We make use of an approach inspired by Monte Carlo integration to estimate local normals as well as curvatures, based on which the force can be calculated. We compare different sampling schemes for the Monte Carlo approach, for which a Halton sequence performed best. Our overall technique is applicable, but not limited to 2D and 3D simulations, and can be coupled with any common SPH formulation. It outperforms prior approaches with regard to total computation time per time step in dynamic scenes. Additionally, it is adjustable for higher quality in small scale scenes with dominant surface tension effects.
In the context of face recognition systems, liveness test is a binary classification task aiming at distinguishing between input images that come from real people’s faces and input images that come from photos or videos of those faces, and presented to the system’s camera by an attacker. In this paper, we train the state-of-the-art, general purpose deep neural network ResNet for liveness testing, and measure the effect on its performance of adversarial attacks based on the manipulation of the saturation component of the imposter images. Our findings suggest that higher saturation values in the imposter images lead to a decrease in the network’s performance. Next, we study the relationship between the proposed adversarial attacks and corresponding direct presentation attacks. Initial results on a small dataset of processed images which are then printed on paper or displayed on an LCD or a mobile phone screen, show that higher saturation values lead to higher values in the network’s loss function, indicating that these colour manipulation techniques can indeed be converted into enhanced presentation attacks.
Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.
Animating a novel character not only needs a lot of effort and time but also requires the intervention of an experienced user. Moreover, the traditional animation pipeline for a set of characters can be a tedious and cumbersome process which often needs to be repeated several times to correct artifacts. We propose a user-friendly, semi-automated efficient method which is realized in two phases: (i) mesh correspondence, and (ii) skeleton and skinning transfer. We have developed a software tool for realizing the entire process without the need of third party software. We have improved the efficiency of the entire animation transfer process. To substantiate this, we provide a comparative performance evaluation with previous competent approaches
