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27 results for “video generation”
Unitymol demo on generating 360 degree videos
<p>This video provides more detailed supportive information about using Unitymol.</p> <p>It requires to run UnityMol from the Unity editor. It will not work with the UnityMol executable.</p> <p>1) from within the Unity editor with an opened UnityMol project, open the Recorder window<br> 2) add a new Movie recorder, setting parameters as shown in the tutorial video<br> 3) launch the UnityMol project (play mode) and start recording<br> 4) do what you want to be captured within UnityMol (can be live action, can be executing a pre-recorded script ..)<br> 5) stop recording when finished</p>
An Open Access User Generated Video Dataset from 2016 Edinburgh Festival
<p>A user generated video dataset captured during the 2016 Edinburgh festival. The provided dataset was collected using a smart phone and is available with no post-processing. The videos mainly cover the Edinburgh streets and the festival atmosphere, and do not cover any performances. The dataset can be used for evaluation of various research tools, such as video quality assessment and enhancement.</p>
Nantes-MobileHDRVQA Dataset: Video Quality of User Generated Mobile HDR Videos
<div>Nantes-MobileHDRVQA Dataset contains 60 source videos (SRC), each compressed with AV1 codec at different bitrate and resolution pairs. More info in ReadMe file.</div> <div>A subjective experiment with Absolute Category Rating with Hidden Reference (ARC-HR) protocol was conducted to collect video quality ratings in the range of (1, 5) where higher values indicate better video quality.</div> <div>The experiments were conducted in laboratory conditions at the facilities of Nantes University, France. </div> <div>For each playlists, individual subjective opinion scores and MOS, DMOS, and 95% CI of the MOS is given for each playlists in corresponding playlists</div> <div>Moreover, playlists are combined in the plistall_ACR.csv file with their MOS, DMOS, and 95% CI of the MOS scores. </div>
Exploring multi-camera views from user-generated sports videos
<div> <p><strong>The proliferation of mobile devices</strong> with video recording capabilities has revolutionized the creation, sharing, and consumption of audiovisual content, turning user-generated video (UGV) platforms into major data sources. </p> <div> <div> <p><strong>Despite this growth</strong>, there is a notable gap in the availability of public datasets featuring multi-angle recordings of sports events captured by various mobile cameras. This led to the creation of the <strong>MUVY Dataset</strong>, with the name stemming from <strong>Multiview User-generated Videos from YouTube.</strong></p> <div> <div> <p>The dataset offers a diverse collection of sports videos from multiple perspectives, without restrictions on video size. In its first version, it covers sports like, <strong>American football, artistic gymnastics, athletics, basketball, tennis, and cricket.</strong></p> <div> <div> <div> <p>The dataset addresses common challenges in user-generated videos, such as shaking, occlusions, blurring, and abrupt movements. Each video is accompanied by metadata including camera identification, YouTube URLs, extracted frames, and object annotations.</p> </div> </div> </div> </div> </div> </div> </div> </div>
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17
Supplementary videos for "Active flow network generates molecular transport by packets: case of the endoplasmic reticulum"
<p>Videos showing simulated motion on the active flow network for different switching timescales. In particular, compare <span class="math-tex">\(\tau_{\text{switch}} = 3 \text{ s}\)</span> to <span class="math-tex">\(\tau_{\text{switch}} = 30 \text{ ms}\)</span>. The red bubbles are proportional to the number of particles present in a node. Initially, all particles are placed in a central source node.</p>
A Dataset of User Generated Videos from Edinburgh Festival 2016
<p>Files include footage from performances of the trEd Dance group (edfest8, edfest9 and edfest10) and Rebecca Wilson’s ‘The Strawberry Show’ (edfest6 and edfest7) taken during Edinburgh Festival 2016 by BBC R&D as part of the COGNITUS project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687605. BBC©2016. Further details about the content are available in the accompanying paper <em>"An Open Access Dataset of User Generated Videos from Edinburgh Festival 2016"</em>. If you have any queries please contact BBC R&D at cognitus-h2020@rd.bbc.co.uk. This notice must remain attached to any copy of the content.</p> <p> </p>
Sample video of 2pL droplet generation.
<p>Sample video of 2pL droplet generation. Can be opened using ImageJ.</p>
Artificially-generated Lecture Video Fragmentation Dataset and Ground Truth
<p>We provide a large-scale lecture video dataset consisting of artificially-generated lectures, and the corresponding ground-truth fragmentation, for the purpose of evaluating lecture video fragmentation techniques.</p> <p>For creating this dataset, 1498 speech transcript files (generated automatically by ASR software) were used from the world's biggest academic online video repository, the VideoLectures.NET. These transcripts correspond to lectures from various fields of science, such as Computer science, Mathematics, Medicine, Politics etc. In order to create the synthetic video lectures, all transcripts were randomly split in fragments, the duration of which ranges between 4 and 8 minutes. Each synthetic lecture was then assembled by combining (stitching) exactly 20 randomly selected fragments. 300 such artificially-generated lectures are included in the released dataset. Each such lecture file has a mean duration of about 120 minutes, thus the dataset contains altogether about 600 hours of artificially-generated lectures. Every pair of consecutive fragments in these lectures originally comes from different videos, consequently the point in time where such two fragments are joined is a known ground-truth fragment boundary. All these boundaries form the dataset's ground truth. We should stress that we do not generate the corresponding video files for the artificially-generated lectures (only the transcripts), and one should not try to reverse-engineer the dataset creation process so as to use in some way the visual modality for detecting the fragments in this dataset.</p> <p><strong>File format</strong></p> <p>After you download the provided .zip and unpack it, the extracted folder will contain two sub-folders:</p> <pre><code>1. ALV_srt 2. ALV_srt_GT </code></pre> <p>Each of them contains 300 files.</p> <p>The <strong>ALV_srt</strong> folder contains the transcripts of every artificially-generated lecture, in the standard SRT format:</p> <pre><code>1. A numeric counter identifying each sequential subtitle 2. The time that the subtitle should appear on the screen, followed by --> and the time it should disappear 3. Subtitle's text itself on one or more lines 4. A blank line containing no text </code></pre> <p>The <strong>ALV_srt_GT</strong> folder contains the ground truth (GT) fragments corresponding to the lectures (transcripts) of the <strong>ALV_srt</strong> folder. Each GT file consists of 3 tab-separated columns and 20 rows, in the following format:</p> <pre><code><Fragment_ID_1> <StartTime_1> <EndTime_1> <Fragment_ID_2> <StartTime_2> <EndTime_2> <Fragment_ID_3> <StartTime_3> <EndTime_3> . . . <Fragment_ID_20> <StartTime_20> <EndTime_20> </code></pre> <p>Each row indicates a fragment. The first column indicates the ID of a fragment while the second and the third column indicate the start and the end time of the fragment respectively.</p> <p><strong>License and Citation</strong></p> <p>This dataset is provided for academic, non-commercial use only. If you find this dataset useful in your work, please cite the following publication where the dataset is introduced:</p> <p><em>D. Galanopoulos, V. Mezaris, “Temporal Lecture Video Fragmentation using Word Embeddings”, Proc. 25th Int. Conf. on Multimedia Modeling (MMM2019), Thessaloniki, Greece, Jan. 2019.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreement No 693092 MOVING. We are grateful to JSI/VideoLectures.NET for providing the lectures’ transcripts.</p>
Behavioral probabilities and sample videos for "Behavioral evidence for nested central pattern generator control of Drosophila grooming"
Open the record for dataset details and reuse information.
Videos related to: Noise matters: Elephants show risk-avoidance behaviour in response to human-generated seismic cues
Open the record for dataset details and reuse information.
Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer: TerrainSkin Video
<p><strong><em>Terrain</em>Skin:4DFlow</strong> is a three-hour durational response to a three-screen dance video installation, <em>Terrain</em>Skin, currently screening in the Dance Interactive Space in Firkin Crane, Cork. <em>Terrain</em>Skin explores the symbiotic relationship between body-mind and environment. This live response through the mediums of movement, video and visual art, allows insight into an evolving creative process, a process of becoming, a process of construction, a process of re-configuration, a process of transition from site-specific environments to a performance installation environment. This live response aims to dissolve the boundaries between performer and audience, through creating a shared immersive interactive space of heightened kinaesthetic and energetic awareness.</p> <p>Mairéad Vaughan – Movement<br> Carolyn Collier – Visual Art<br> Aoife Desmond – Visual Art<br> Dara O’Brien – Video</p> <p>This video, and another <strong><em>Terrain</em>Skin4DFlow Performance</strong> (<a href="http://doi.org/10.5281/zenodo.2613568">http://doi.org/10.5281/zenodo.2613568</a>), form part of a PhD thesis held in the University of Limerick Institutional Repository (ULIR):</p> <p>Vaughan, M. (2016) <em>'Generating states: exploring the kinaesthetic and energetic relationship between the moving body-mind and environment, through the lens of a dancer, a choreographer and a performer'</em>, University of Limerick (PhD Thesis). Available from: <a href="https://ulir.ul.ie/handle/10344/5217">https://ulir.ul.ie/handle/10344/5217</a>.</p> <p>The research was funded by the Arts Council of Ireland.</p>
The New Third Generation Video Laryngeal Mask Evaluation
ClinicalTrials.gov study NCT07007403. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Video-Based AI-Generated Peri-operative Instructions for Mitral Valve Surgery
ClinicalTrials.gov study NCT07036926. IPD Sharing: NO. Countries: 0. Publications: 10.
Video 2 from: Hita Garcia F, Fischer G, Liu C, Audisio TL, Economo EP (2017) Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics. ZooKeys 693: 33-93. https://doi.org/10.3897/zookeys.693.13012
Video 2 - 3D rotation video of Zasphinctus sarowiwai sp. n. paratype worker (CASENT0764650) based on shaded volumetric surface rendering of full body.
Video 5 from: Hita Garcia F, Fischer G, Liu C, Audisio TL, Economo EP (2017) Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics. ZooKeys 693: 33-93. https://doi.org/10.3897/zookeys.693.13012
Video 5 - 3D rotation video of full body of Zasphinctus sarowiwai sp. n. holotype worker (CASENT0764654). False-colour volume rendering of segmented mesosoma and metasoma musculature (red) and sting apparatus (green) superimposed on semitransparent surface model.
Video 4 from: Hita Garcia F, Fischer G, Liu C, Audisio TL, Economo EP (2017) Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics. ZooKeys 693: 33-93. https://doi.org/10.3897/zookeys.693.13012
Video 4 - 3D rotation video of segmented surface reconstructions of the mouthparts of Zasphinctus sarowiwai sp. n. (CASENT0764652) in open configuration (green= maxillae; yellow=labrum; orange=labium).
Video 3 from: Hita Garcia F, Fischer G, Liu C, Audisio TL, Economo EP (2017) Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics. ZooKeys 693: 33-93. https://doi.org/10.3897/zookeys.693.13012
Video 3 - 3D rotation video of Zasphinctus wilsoni sp. n. holotype worker (MCZ-ENT-00512764) based on shaded volumetric surface rendering of full body.
Video 1 from: Hita Garcia F, Fischer G, Liu C, Audisio TL, Economo EP (2017) Next-generation morphological character discovery and evaluation: an X-ray micro-CT enhanced revision of the ant genus Zasphinctus Wheeler (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics. ZooKeys 693: 33-93. https://doi.org/10.3897/zookeys.693.13012
Video 1 - 3D rotation video of Zasphinctus obamai sp. n. holotype worker (CASENT0764125) based on shaded volumetric surface rendering of full body.
Video - Assembly Task Modelling Strategy for Automatic Robot Program Generation
<p>Video - Assembly Task Modelling Strategy for Automatic Robot Program Generation</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.