CAMERA 2.0

相机2.0

• 批准号: EP/T022523/1
• 负责人: Darren Cosker
• 金额: $ 433.44万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT022523%2F1
• 关键词: CAMERA; 2.0

Intelligent Visual and Interactive Technology allows us to perceive, understand and re-create the world around us. With it we can digitise the world with 3D cameras, use Artificial Intelligence (AI) to predict and enhance the health of people within our world or to educate and train them. It allows us to experience this world, or imagined ones, through immersive technologies, movies and video games, and interact with these worlds through technologies that analyse our movement and behaviour. There is a clear benefit to applying this technology across domains, for specific health or education purposes, but doing so requires coordinated action and genuine democratisation of the underpinning technologies, such that non-expert users are empowered. To address this challenge, CAMERA 2.0 will perform world-leading research in Intelligent Visual and Interactive Technology - underpinned by academic and partner expertise across Computer Vision, Computer Graphics, Human Computer Interaction (HCI) and AI - and engage a range of partners to generate impact and translate this technology across a range of themes. This multi-disciplinary approach is supported by academic and external partner expertise spanning healthcare, biomechanics, sports performance and psychology. These collaborations will allow us to carry out new research, create new impacts and develop further partnerships that would otherwise be impossible to achieve.This proposal builds on our highly successful Next Stage Digital Economy Centre for the Analysis of Motion Entertainment Research and Applications (CAMERA). Over the last 4 years, we have built a team of 14 academics and over 40 PhDs and researchers who have created real impact, alongside our partners, across themes of i) Entertainment; ii) Health, Rehabilitation and Assistive Technologies and; iii) Human Performance Enhancement.CAMERA 2.0 will also focus on three themes, supported by over 20 impact partners: i) Creative Science and Technology, ii) Digital Health and Assistive Technology andiii) Human Performance Enhancement. Furthermore, CAMERA 2.0 will work closely with our EPSRC CDT in Digital Entertainment and our new UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI). Our research programme will deliver continuing impact through four primary mechanisms: (i) Theme Driven Impact Projects, (ii) Cross-Cutting Theme R&D Challenges, (iii) Reactive Impact Projects and (iv) Open Community Engagement.Theme Driven Impact Projects will be 12 to 24-month projects co-designed through sand-pits and co-delivered with partners. Although primarily aligned with a single theme they will overlap with at least one other.Our Cross-Cutting Theme R&D Challenges engage with R&D challenges shared by partners/academics across themes. Translating innovations across themes not only democratises and accelerates technology adoption but can significantly enhance impact. This will be addressed through key research projects, that support and feed into all other activities.Our reactive model allows us to carry out commercial projects as research impact vehicles at short notice - essential being able to work with the short-deadline driven creative sector. CAMERA 2.0 evolves our unique reactive impact model by placing our CAMERA student technical team at its core under the supervision of our experienced studio managers. Impact through Open Engagement. Our ambition is to raise the level of UK and international DE research through collaboration and technology democratisation. CAMERA 2.0 will operate an open-door model for reasonable access to facilities, data, software and training. In coordination with commitments from the University of Bath and external EU funding we are expanding our physical facilities and technical team to provide assisted motion capture and immersive technology training for free to over 100 creative industries, HEIs and healthcare companies.

智能视觉和交互技术让我们能够感知、理解和重新创造我们周围的世界。有了它，我们可以用3D相机数字化世界，使用人工智能(AI)来预测和提高我们世界上人们的健康，或者教育和培训他们。它允许我们通过身临其境的技术、电影和视频游戏体验这个世界或想象中的世界，并通过分析我们的运动和行为的技术与这些世界互动。为了特定的健康或教育目的，跨领域应用这项技术有明显的好处，但要做到这一点，需要采取协调行动，并真正实现支撑技术的民主化，以便赋予非专家用户权力。为了应对这一挑战，Camera 2.0将在智能视觉和交互技术领域开展世界领先的研究--以计算机视觉、计算机图形学、人机交互(HCI)和人工智能领域的学术和合作伙伴专业知识为基础--并与一系列合作伙伴合作，在一系列主题上产生影响并转化这项技术。这种多学科的方法得到了学术和外部合作伙伴专业知识的支持，这些专业知识涵盖医疗保健、生物力学、运动表现和心理学。这些合作将使我们能够开展新的研究，创造新的影响，并发展更多的合作伙伴关系，否则是不可能实现的。这项建议建立在我们非常成功的下一阶段数字经济中心运动娱乐研究和应用分析(CAMERA)的基础上。在过去的4年里，我们建立了一支由14名学者和40多名博士和研究人员组成的团队，他们与我们的合作伙伴一起，在i)娱乐；ii)健康、康复和辅助技术以及iii)人类表现增强的主题上产生了真正的影响。CAMERA 2.0还将专注于三个主题，得到20多个Impact合作伙伴的支持：i)创意科学和技术，ii)数字健康和辅助技术以及ii)人类表现增强。此外，Camera 2.0将与我们在数字娱乐领域的EPSRC CDT和我们新的UKRI CDT在负责任、负责任和透明的人工智能(ART-AI)方面密切合作。我们的研究方案将通过四个主要机制产生持续的影响：(I)主题驱动的影响项目，(Ii)交叉主题研发挑战，(Iii)反应性影响项目和(Iv)开放社区参与。主题驱动的影响项目将是通过沙坑共同设计并与合作伙伴共同交付的为期12至24个月的项目。虽然主要与单一主题保持一致，但它们至少会相互重叠。我们的横向主题研发挑战涉及合作伙伴/学者跨主题共享的研发挑战。跨主题转换创新不仅可以促进和加速技术的采用，而且还可以显著增强影响力。这将通过支持和支持所有其他活动的关键研究项目来解决。我们的反应性模式使我们能够在短时间内作为研究影响工具开展商业项目--这是能够与期限较短的创意部门合作的关键。Camera 2.0通过将我们的相机学生技术团队置于其核心，在我们经验丰富的工作室经理的监督下，发展了我们独特的反应性影响模型。通过Open Engagement产生影响。我们的雄心是通过合作和技术民主化来提高英国和国际DE研究的水平。Camera 2.0将以开放模式运行，以便合理地访问设施、数据、软件和培训。在巴斯大学的承诺和欧盟的外部资助下，我们正在扩大我们的物理设施和技术团队，为100多个创意产业、高等院校和医疗保健公司免费提供辅助动作捕捉和身临其境的技术培训。

项目成果

Musicians' views on the role of reading music in learning, performance, and understanding

音乐家对阅读音乐在学习、表演和理解中的作用的看法

• DOI: 10.1177/10298649221149110
• 发表时间: 2023
• 期刊: Musicae Scientiae
• 影响因子: 2.4
• 作者: Arthurs Y

Agonistic Memory and the Legacy of 20th Century Wars in Europe

竞争性记忆与 20 世纪欧洲战争的遗产

• DOI: 10.1007/978-3-030-86055-4_5
• 发表时间: 2021
• 作者: Angeli D

Full-Body Motion from a Single Head-Mounted Device: Generating SMPL Poses from Partial Observations

• DOI: 10.1109/iccv48922.2021.01148
• 发表时间: 2021-10
• 期刊: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Andrea Dittadi;Sebastian Dziadzio;D. Cosker;Bengt Lundell;Tom Cashman;J. Shotton

Effect of carbohydrate-protein supplementation on endurance training adaptations.

• DOI: 10.1007/s00421-020-04450-1
• 发表时间: 2020-10
• 期刊: European journal of applied physiology
• 影响因子: 3
• 作者: Alghannam AF;Templeman I;Thomas JE;Jedrzejewski D;Griffiths S;Lemon J;Byers T;Reeves S;Gonzalez JT;Thompson D;Bilzon J;Tsintzas K;Betts JA
• 通讯作者: Betts JA

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

• DOI: 10.1007/978-3-030-58452-8_26
• 发表时间: 2020-08
• 期刊: ArXiv
• 作者: Benjamin Attal;Selena Ling;Aaron Gokaslan;Christian Richardt;J. Tompkin