Autodesk

Head tracking is commonly used in VR applications to allow users to naturally view 3D content using physical head movement, but many applications also support turning with hand-held controllers. Controller and joystick controls are convenient for practical settings where full 360-degree physical rotation is not possible, such as when the user is sitting at a desk. Though controller-based rotation provides the benefit of convenience, previous research has demonstrated that virtual or joystick-controlled view rotation to have drawbacks of sickness and disorientation compared to physical turning. To combat such issues, researchers have considered various techniques such as speed adjustments or reduced field of view, but data is limited on how different variations for joystick rotation influences sickness and orientation perception. Our studies include different variations of techniques such as joystick rotation, resetting, and field-of-view reduction. We investigate trade-offs among different techniques in terms of sickness and the ability to maintain spatial orientation. In two controlled experiments, participants traveled through a sequence of rooms and were tested on spatial orientation, and we also collected subjective measures of sickness and preference. Our findings indicate a preference by users towards directly-manipulated joystick-based rotations compared to user-initiated resetting and minimal effects of technique on spatial awareness.

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Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.

We present VideoPoseVR, a video-based animation authoring workflow using online videos to author character animations in VR. It leverages the state-of-the-art deep learning approach to reconstruct 3D motions from online videos, caption the motions, and store them in a motion dataset. Creators can import the videos, search in the dataset, modify the motion timeline, and combine multiple motions from videos to author character animations in VR. We implemented a proof-of-concept prototype and conducted a user study to evaluate the feasibility of the video-based authoring approach as well as gather initial feedback of the prototype. The study results suggest that VideoPoseVR was easy to learn for novice users to author animations and enable rapid exploration of prototyping for applications such as entertainment, skills training, and crowd simulations.

Extreme polarization of opinions fuels many of the problems facing our societies today, from issues on human rights to the environment. Social media provides the vehicle for these opinions and enables the spread of ideas faster than ever before. Previous computational models have suggested that significant external events can induce extreme polarization. We introduce the Social Opinion Amplification Model (SOAM) to investigate an alternative hypothesis: that opinion amplification can result in extreme polarization. SOAM models effects such as sensationalism, hype, or “fake news” as people express amplified versions of their actual opinions, motivated by the desire to gain a greater following. We show for the first time that this simple idea results in extreme polarization, especially when the degree of amplification is small. We further show that such extreme polarization can be prevented by two methods: preventing individuals from amplifying more than five times, or through consistent dissemination of balanced opinions to the population. It is natural to try and have the loudest voice in a crowd when we seek attention; this work suggests that instead of shouting to be heard and generating an uproar, it is better for all if we speak with moderation.

Integration of advanced technologies have revitalised treatment methods in the current clinical practice. In orthopaedic surgery, patient-specific implants have leveraged the design freedom offered by additive manufacturing (AM) exploiting the capabilities within powder bed fusion processes. Furthermore, generative design (GD), a design exploration tool based on the artificial intelligence, can integrate manufacturing constraints in the concept development phase, consequently bridging the gap between AM design and manufacturing. However, the reproducibility of implant prototypes are severely constrained due to uncomprehensive information on manufacturing and post processing techniques in the detailed design phase. This paper explores the manufacturing feasibility of novel GD concept plate designs for High Tibial Osteotomy (HTO), a joint preserving surgery for a patient diagnosed with osteoarthritis in the knee. A design for AM (DfAM) workflow for a generatively designed HTO plate is presented, including; detailed DfAM of GD concept designs, fabrication of plate prototypes using electron beam powder bed fusion (PBF-EB) of medical grade Ti-6Al-4 V, post processing and inspection. The study established PBF-EB as a suitable manufacturing method for the highly complex GD plate fixations, through evaluating the impact of manufacturing and post processing on the surface finish and geometrical precision of the plate design features.

Real-world design problems are a messy combination of constraints, objectives, and features. Exploring these problem spaces can be defined as a Multi-Criteria Exploration (MCX) problem, whose goals are to produce a set of diverse solutions with high performance across many objectives, while avoiding low performance across any objectives. Quality-Diversity algorithms produce the needed design variation, but typically consider only a single objective. We present a new ranking, T-DominO, specifically designed to handle multiple objectives in MCX problems. T-DominO ranks individuals relative to other solutions in the archive, favoring individuals with balanced performance over those which excel at a few objectives at the cost of the others. Keeping only a single balanced solution in each MAP-Elites bin maintains the visual accessibility of the archive – a strong asset for design exploration. We illustrate our approach on a set of easily understood benchmarks, and showcase its potential in a many-objective real-world architecture case study. KeywordsQuality-diversityGenerative designMulti-objective

Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired target. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.

Plain Language Summary Wide‐swath satellite observations of Sea Surface Height (SSH) data at high spatial resolutions will be available in abundance thanks to advances of instrumental technologies. Embedded in the observed SSH are internal tides, a dynamical component that plays a crucial role in ocean circulation. As they are entangled with background currents and eddies, such tidal signals are challenging to extract. Methods that worked with previous‐generation altimeters will break down at the resolutions that the new generation promises. On the other hand, the wide satellite swaths provide new opportunities as they allow us to regard the observations as spatially two‐dimensional. Here we treat the tidal extraction solely as an image translation problem. We train a deep neural net so that given a snapshot of a raw SSH signal, it produces a “fake” snapshot of the tidal SSH signal that is meant to reproduce the original. The data we use in this article is generated by idealized numerical simulations. Once adapted to realistic data, the network has the potential to become a new tidal extraction tool for satellite observations. More broadly, successes in our experiments can inspire other applications of generative networks to disentangle dynamical components in data where classical analysis may fail.