Realizing Semantic Virtual Environments with Ontology and Pluggable Procedures

Multi-User Virtual Environment (MUVE) has attracted much attention recently due to the increasing number of users and potential applications. Fig. 1 shows the common components that a MUVE system may provide. Generally speaking, a MUVE refers to a virtual world that allows multiple users to log in concurrently and interact with each othe

Task-driven influences on fixational eye movements

There is now compelling evidence that the spatiotemporal remapping carried out by fixational eye movements (FEMs) is an essential step in visual processing. Moreover, the overall Brownian-like statistics of FEMs are calibrated to map fine spatial detail into the temporal frequency range to which retinal circuitry is tuned. Here, we tested the hypothesis that the detailed spatial characteristics of FEMs can be adjusted to task demands via cognitive influences that operate even in the absence of a visual stimulus. We examined FEMs in a task that required subjects (N=6) to report which of two letters was displayed. Trials were blocked; in each block, the letter pair was known in advance: H vs. N or E vs. F. The task was demanding: letters were 1.5 deg and embedded in 1/f noise, and had a contrast that yielded ~75% correct performance. Note that the HN discrimination could be accomplished by identification of either a horizontal or oblique contour, but the EF discrimination required identification of a horizontal contour. Thus, in the EF blocks, only a vertical ocular drift would be expected to maximize the neural signal. For each condition, FEM velocity statistics, which were approximately Gaussian, were characterized by their covariance. As predicted, the ratio of velocity variance in the vertical vs. oblique direction was greater in EF trials than in HN trials. This difference was greater when no stimulus was present (20% of trials in each block), indicating open-loop control. We also found that single-trial drift trajectories could be decoded by a simple decoder to identify the task (HN vs. EF) at above-chance levels in most subjects. While the observed covariance patterns showed substantial inter-subject variability, we found that a single transformation, applied with subject-specific strengths, could largely account for all subjects’ findings. Critically, this shared transformation acts holistically on the plane, rather than individually on horizontal and vertical axes. In sum, we find that knowledge of the specific requirements of a visual task exerts fine-tuned open-loop control over ocular drifts, and we characterize the nature of this control

Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models

In this study, we present an efficient and effective approach for achieving temporally consistent synthetic-to-real video translation in videos of varying lengths. Our method leverages off-the-shelf conditional image diffusion models, allowing us to perform multiple synthetic-to-real image generations in parallel. By utilizing the available optical flow information from the synthetic videos, our approach seamlessly enforces temporal consistency among corresponding pixels across frames. This is achieved through joint noise optimization, effectively minimizing spatial and temporal discrepancies. To the best of our knowledge, our proposed method is the first to accomplish diverse and temporally consistent synthetic-to-real video translation using conditional image diffusion models. Furthermore, our approach does not require any training or fine-tuning of the diffusion models. Extensive experiments conducted on various benchmarks for synthetic-to-real video translation demonstrate the effectiveness of our approach, both quantitatively and qualitatively. Finally, we show that our method outperforms other baseline methods in terms of both temporal consistency and visual quality

MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance

This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow. The proposed framework can render videos from scene position information, such as a normal G-buffer, or perform text-guided editing on videos captured in real-world scenarios. We employ explicit optical flows to construct a practical coding that enforces physical constraints on generated frames and mediates independent frame-wise scores. By leveraging this coding, maintaining temporal consistency in the generated videos can be framed as an optimization problem with a closed-form solution. To ensure compatibility with Stable Diffusion, we also suggest a workaround for modifying observed-space scores in latent-space Diffusion Models. Notably, MeDM does not require fine-tuning or test-time optimization of the Diffusion Models. Through extensive qualitative, quantitative, and subjective experiments on various benchmarks, the study demonstrates the effectiveness and superiority of the proposed approach

Social consequences of mass quarantine during epidemics: a systematic review with implications for the COVID-19 response.

Four billion people worldwide have experienced coronavirus disease 2019 (COVID-19) confinement. Such unprecedented extent of mobility restriction to curb the COVID-19 pandemic may have profound impacts on how individuals live, travel and retain well-being. This systematic review aims to identify (i) the social consequences of mass quarantine-community-wide movement restrictions-during previous and current infectious disease outbreaks and (ii) recommended strategies to mitigate the negative social implications of COVID-19 lockdowns. Considering social determinants of health, we conducted a systematic review by searching five databases (Ovid-MEDLINE, EMBASE, PsycINFO, China National Knowledge Infrastructure and the World Health Organization COVID-19 database) for publications from inception to 9 April 2020. No limitation was set on language, location or study type. Studies that (i) contained peer-reviewed original empirical evidence and (ii) focussed on non-epidemiological implications of mass quarantine were included. We thematically synthesized and reported data due to heterogeneous disease and country context. Of 3067 publications found, 15 original peer-reviewed articles were selected for full-text extraction. Psychological distress, heightened communication inequalities, food insecurity, economic challenges, diminished access to health care, alternative delivery of education and gender-based violence were identified as negative social consequences of community-based quarantine in six infectious disease epidemics, including the current COVID-19 pandemic. In contrast, altruistic attitudes were identified as a positive consequence during previous quarantines. Diverse psychological and social consequences of mass quarantine in previous and current epidemics were evident, but individual country policies had been highly varied in how well they addressed the needs of affected individuals, especially those who are socially marginalized. Policymakers should balance the pros and cons of movement restrictions, facilitate multisectoral action to tackle social inequalities, provide clear and coherent guidance to the public and undertake time-bound policy evaluations to mitigate the negative impact of COVID-19 lockdowns and to establish preparedness strategies for future epidemics