جست‌وجوی استاد و پژوهشگر

Carl Vondrickمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Multimodal Perception
• +۱۱ مورد دیگر

Carl Vondrick is a Professor in the Department of Computer Science at Columbia University. His research focuses on creating robust and versatile perception systems that leverage video and interaction with the natural world, with applications in 3D reconstruction, visual question answering, and robot manipulation. Former research scientist at Google Visiting researcher at Cruise Education: PhD (2017) from MIT, advised by Antonio Torralba BS (2011) from UC Irvine, advised by Deva Ramanan His research explores multimodal approaches for cross-task and cross-modal transfer, scene dynamics, audiovisual perception, interpretable models, and spatial awareness systems. The lab emphasizes zero-shot generalization and neuro-symbolic methods while addressing safety and robustness in AI systems. Key publication trends include: 2025: Video generation for robotics 2024: Differentiable rendering and cross-modal reasoning 2023: Robust perception and 3D modeling Scientific Awards: 2024 PAMI Young Researcher Award 2021 NSF CAREER Award Teaching Roles: Teaching Computer Vision II (2021-2025), Computer Vision I (2018-2019), and Representation Learning (2020-2022). Advising: Advises 8 current PhD students and has mentored 5 graduated students now at institutions like MBZUAI and UMD. The lab recruits 1-2 PhD students annually through Columbia’s PhD program. Grants and Collaborations: Funded by NSF, DARPA, Toyota Research Institute, Amazon Research, and Google.

Adam Runionsمشاهده پروفایل

پژوهشگر

• Computational Modeling
• Developmental Biology
• Computer Science
• +۴ مورد دیگر

Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.

Jonathan T. Barronمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative AI
• +۳ مورد دیگر

Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .

• Computer Vision
• Machine Learning
• 3D Human Modeling
• +۴ مورد دیگر

Michael J. Black is a Professor and Honorarprofessor at the University of Tübingen's Faculty of Science, Department of Computer Science, and a founding Director of the Max Planck Institute for Intelligent Systems, leading the Perceiving Systems department. He holds a B.Sc. from the University of British Columbia (1985), M.S. from Stanford (1989), and Ph.D. in Computer Science from Yale (1992). His research focuses on computer vision, 3D human modeling, motion capture, and AI-driven digital humans. Key contributions include the SMPL body model, optical flow algorithms, and datasets like Middlebury Flow and Sintel. He has received major awards such as the PAMI Distinguished Researcher Award, multiple Koenderink and Longuet-Higgins Prizes, and is a member of the German National Academy of Sciences Leopoldina and Royal Swedish Academy of Sciences. His commercial ventures include co-founding Body Labs (acquired by Amazon) and Meshcapade, advancing 3D human generation and interaction technologies. Recent work includes markerless motion capture systems (e.g., MAMMA, PICO), 3D hair and garment synthesis, and AI tools like ChatHuman for 3D human interaction analysis. His research bridges vision, graphics, and robotics, with applications in animation, healthcare, and robotics.

• Computer Vision
• 3D Human Modeling
• Digital Humans
• +۵ مورد دیگر

Michael J. Black is a Professor and Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department and serves as Managing Director . He is also an Honorarprofessor at the University of Tübingen 's Faculty of Science . His career spans roles at Brown University (2000-2010), Xerox PARC, and academic-industry collaborations with Amazon and Meshcapade.

Xiaoxiao Longمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Computer Vision
• 3D Vision
• +۴ مورد دیگر

Xiaoxiao Long is a Tenure-Track Associate Professor at the School of Intelligence Science and Technology, Nanjing University. He joined NJU as an associate professor in February 2024. Previously, he earned his Ph.D. from the University of Hong Kong (HKU) under the supervision of Prof. Wenping Wang (IEEE & ACM Fellow) and Prof. Taku Komura. His educational background includes: Ph.D. in Computer Science from University of Hong Kong Bachelor's degree in Control Science & Engineering from Zhejiang University Dr. Long's research focuses on computer graphics and 3D computer vision, with particular emphasis on 3D Vision, Physical AI, and World Models. His long-term goal is to develop General-Purpose AI with spatial capabilities. His work bridges theoretical understanding of 3D spaces with practical implementations of spatial AI systems, with applications spanning robotics, virtual reality, and augmented environments. He employs innovative neural network approaches and geometric constraints to advance 3D scene understanding and reconstruction. His publication record shows strong momentum with multiple papers accepted to top-tier conferences including CVPR (5 papers in 2025 alone), ICML, ICLR, ECCV, and TPAMI. His research demonstrates a clear progression from foundational geometric estimation techniques (ASN++) toward more comprehensive spatial AI systems. His scientific recognition includes: Excellent Young Scholars Fund (Overseas) from NSFC Dr. Long has successfully mentored numerous students who have published at major venues and gone on to pursue advanced degrees at prestigious institutions including USTC, Beihang University, HKU, UCAS, Virginia Tech, and HKUST. He is currently recruiting Ph.D. and master's students for Fall 2026, seeking candidates interested in pushing the boundaries of 3D computer vision and spatial AI. His laboratory focuses on developing advanced techniques for 3D scene understanding, neural rendering, and physical AI. Current projects span Gaussian-based representations, neural radiance fields, and geometric estimation, with applications in robotics, virtual environments, and spatial reasoning systems.

• Machine Learning
• Reinforcement Learning
• Robotics
• +۳ مورد دیگر

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

Benjamin Busamمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Multi-Modal Sensor Fusion
• Collaborative Robotics
• +۶ مورد دیگر

Dr. Benjamin Busam is a Senior Research Scientist at the Technical University of Munich , affiliated with the Chair for Computer Science Applications in Medicine under Prof. Nassir Navab. Starting September 2025, he will hold the Professorship for Photogrammetry and Remote Sensing at TUM. His career includes leadership roles at FRAMOS Imaging Systems and Huawei Research in London. Education: Mathematics (TUM), Mathematics and Physics (ParisTech, University of Melbourne), PhD in Mathematics (TUM, 2014) His research focuses on 3D computer vision , multi-modal sensor fusion , and their applications in collaborative robotics and augmented reality . He specializes in projective geometry , 6D pose estimation , and neural radiance fields , with a particular emphasis on photometrically challenging environments. Recent publications highlight advancements in 3D scene understanding , neural rendering , and medical imaging , often leveraging machine learning and vision-language models . His work has been recognized through awards like the EMVA Young Professional Award (2015) and Innovation Pioneer of the Year (2019) , along with multiple Outstanding Reviewer distinctions at leading conferences. Dr. Busam has supervised numerous PhD and MSc students on topics including 6D pose estimation , medical augmented reality , and robotic ultrasound , collaborating with institutions like MIT , École Polytechnique , and University of Padova .

• Computer Vision
• Computer Graphics
• Machine Learning
• +۳ مورد دیگر

Gerard Pons-Moll is a Professor at the University of Tübingen, endowed by the Carl Zeiss Foundation, and heads the Emmy Noether independent research group 'Real Virtual Humans'. He is a core faculty member at the Tübingen AI Center, a senior researcher at the Max Planck Institute for Informatics (MPII), and faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the Saarland Informatics Campus. His research focuses on computer vision, graphics, and machine learning, particularly in creating virtual human models and analyzing human motion from video and sensor data. Education: PhD (with distinction) in 2014 from Leibniz University of Hannover, Master's in Telecommunications Engineering (Northeastern University, 2008), and B.S./M.Sc. in Telecommunications Engineering from the Technical University of Catalonia (2002–2008). Research Interests: 3D human modeling, pose estimation, human-object interaction, and applications in industry and research. His work emphasizes real-world applications like virtual avatars and motion capture systems. Awards: Emmy Noether Grant (2018), German Pattern Recognition Award (2019), Google Faculty Research Award (2019), and multiple best paper awards at top conferences (BMVC’13, Eurographics’17, 3DV'18, CVPR'20). Advising & Grants: Served as program chair of 3DV 2021, area chair for ECCV, CVPR, and IJCAI. Active in reviewing for DFG, ANR, and ISF. Supervises research in areas like neural rendering frameworks (Blendify) and synthetic data generation (STAGE). Labs/Teams: Leads the Emmy Noether group and collaborates with MPII, Tübingen AI Center, and IMPRS-IS on projects like XNect (real-time 3D motion capture) and Human 3Diffusion (avatar creation).

Zhen Liuمشاهده پروفایل

استادیار

• Generative Models
• 3D Representation Learning
• Computer Vision
• +۲ مورد دیگر

Zhen Liu is an Assistant Professor at the School of Data Science, CUHK-Shenzhen. His research focuses on generative models, 3D representations, and the synergy of spatial and semantic understanding in AI systems. With a PhD from Mila and Université de Montréal, he develops foundational methods for physics simulation, 3D assembly, and semantic reasoning in neural networks. His work bridges machine learning with applications in computer vision and graphics, emphasizing: Generative architectures for 3D content creation Diffusion model alignment techniques Efficient parameter finetuning strategies Dr. Liu mentors students in AI research and contributes to advancing 3D generative modeling paradigms.

Oliver Staadtمشاهده پروفایل

استاد

• Virtual Reality
• Augmented Reality
• Computer Graphics
• +۷ مورد دیگر

Prof. Dr. sc. techn. ETH Oliver Staadt is Full Professor of Computer Science and Chair of Visual Computing at the University of Rostock , Germany. Since 2023 he also serves as Director of the Institute for Visual and Analytic Computing within the Faculty of Computer Science and Electrical Engineering . Previously he was Dean (2016–2018) and Vice Dean (2010–2016) of the same faculty. Education Ph.D. in Computer Science, ETH Zürich (2001) M.Sc. in Computer Science, TU Darmstadt (1994) Research Interests Prof. Staadt’s research spans virtual and augmented reality , computer graphics , visualization , telepresence , immersive analytics , and human–computer interaction . A particular focus lies on real-time rendering and display technologies for large high-resolution display systems, depth-image enhancement for RGB-D sensors, and interaction techniques that leverage spatial cognition and eye-tracking. His work is frequently applied to collaborative settings and microgravity environments, including experiments aboard parabolic flights and the International Space Station. Recent Publication Trends Between 2019 and 2021 his output centers on foveated rendering , AR viewpoint guidance , collaborative analytics on wall-sized displays , and embodied interaction metaphors . Earlier work addressed bandwidth-efficient telepresence, depth-image filtering, and physically-based animation. The corpus reveals a steady evolution from fundamental graphics algorithms toward applied immersive systems. Scientific Awards & Honors Fellow of the Eurographics Association Associate Editor, IEEE Transactions on Visualization and Computer Graphics (past) Associate Editor, Computers & Graphics (past) Associate Editor, Computer Animation and Virtual Worlds (past) Associate Editor, Frontiers in Virtual Reality (current) Chair, Expert Group on Virtual & Augmented Reality, German Informatics Society (2013–2020) Advising & Funding He has successfully supervised more than ten PhD graduates whose dissertations range from collision detection and physically-based animation to 3D interaction in microgravity and predictive user modeling. Current PhD researchers include Bipul Mohanto, Mana Takhsha, and Sven Kluge. His projects are supported by national and EU programs such as EVOCATION, SMOOTH, ARGuide, 3DPick, DIVA, and Telepresence. Labs & Teams Prof. Staadt leads the Visual Computing Group at Rostock, operating state-of-the-art facilities including large tiled display walls, VR/AR laboratories, and motion-capture systems. The institute hosts interdisciplinary collaborations with partners in visualization, computer vision, psychology, and aerospace engineering.

Sebastian von Mammenمشاهده پروفایل

استاد

• Games Engineering
• Virtual Reality
• Interactive Simulations
• +۳ مورد دیگر

Prof. Dr. Sebastian von Mammen is a tenured professor at the University of Würzburg's Institute for Computer Science, where he heads the Games Engineering research group and contributes to the Chair for Human-Computer Interaction. His group leads the Games Engineering academic program. Previously, he completed his habilitation (2012-2016) at the University of Augsburg's Chair of Organic Computing and was a postdoctoral fellow at the University of Calgary. His research spans: Real-Time Interactive Systems : Visual programming, immersion techniques, software engineering Interactive Simulations : Serious games for healthcare/logistics/construction Artificial Life : Self-organisation, adaptive systems, evolutionary computation Artificial Intelligence : Agent-based modeling, procedural content generation Recent publications (2023-2025) demonstrate strong focus on: Virtual reality applications in education (femtoPro optics simulator, BrainBuilder neuroanatomy) Healthcare technology platforms (VIA-VR for medical serious games) Game mechanics analysis (Match-3, Jump'n'Run flow) AI-driven emotion recognition and interactive systems Computational modeling of biological systems He leads the Games Engineering research group and previously participated in the Evolutionary and Swarm Design group (Calgary) and LINDSAY project. His lab develops VR simulations for scientific training and serious games applications.

Yan Zhangمشاهده پروفایل

مدرس

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.

Leif Kobbeltمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +۳ مورد دیگر

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Ayush Tewariمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۳ مورد دیگر

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.