London, U.K.

The London, UK, lab is in the modern White Collar Factory building in the Shoreditch neighborhood. It is positioned on the “Silicon Roundabout” in London’s fast-growing Tech City, a hive of innovation and the third-largest technology cluster in the world after the San Francisco Bay Area and New York City. 

About us Adobe Research

AI & Machine Learning

About us Adobe Research

Computer Vision, Imaging & Video

About us Adobe Research

Content Intelligence

About us Adobe Research

Graphics (2D & 3D)

Meet some of our researchersAll Researchers

Tuanfeng Y. Wang

Research Scientist

Xuelin Chen

Research Scientist

Hyeonho Jeong

Research Engineer

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View our latest publicationsView More

ResEdit: Residual embeddings for precise generative image editing

Baykal, Ahmet., Deschaintre, Valentin., Hold-Geoffroy, Yannick., Fischer, Michael., Frühstück, Anna., Öztireli, Cengiz., Georgiev, Iliyan. (Jun. 15, 2026)

EGSR

UniLight: A Unified Representation for Lighting

Zhang, Zitian., Georgiev, Iliyan., Fischer, Michael., Hold-Geoffroy, Yannick., Lalonde, Jean-François., Deschaintre, Valentin. (Jun. 1, 2026)

Highlight

CVPR

MAOAM: Unified Object and Material Selection with Vision-Language Models

Park, Jaden., Deschaintre, Valentin., Kuen, Jason., Liu, Kangning., Georgiev, Iliyan., Singh, Krishna., Lee, Yong., Fischer, Michael. (Jul. 23, 2026)

SIGGRAPH

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View our latest newsAll News

ECCV 2026: Adobe researchers share experimental AI tools for more precise image and video generation

Wondering what’s next for precise, controllable generative AI? Check out the breakthroughs our Adobe researchers are sharing this week at ECCV.

This World Photography Day, Adobe Research Maps How Trust Travels with a Photo

On World Photography Day, Adobe Researchers are contributing technologies that give people the information they need to understand where an image comes from, how it’s been changed, and whether its history can be independently verified.

TokenTrace at CVPR 2026: Tracing Creative Influence in Generative AI 

TokenTrace embeds lightweight watermarks into training tokens associated with visual concepts. When the model later generates an image, TokenTrace can recover traces of these watermarked signals, allowing researchers to identify which concepts most strongly contributed to the output. 
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