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Jakub Powierza

dblp:401/6351 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0006-0891-0748ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Visual content generation and editing · 88% Rendering · 12%
Artificial intelligence
1 paper
Generative modeling · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization · CVPR 2025
Visual content generation and editing › style transfer
3d content stylization
0.912025
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization · CVPR 2025
Visual content generation and editing › style transfer › 3d content stylization
gaussian splatting stylization
0.912025
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization · CVPR 2025
Immersive interaction
augmented reality
0.912025
ImaginateAR: AI-Assisted In-Situ Authoring in Augmented Reality · UIST 2025
Visual content generation and editing › 3d content generation
3d asset generation
0.312025
ImaginateAR: AI-Assisted In-Situ Authoring in Augmented Reality · UIST 2025
Rendering
novel view synthesis
0.312025
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization · CVPR 2025

Methods — techniques the papers use, named apart from their topics

scene understanding · 1.7large language model · 1.7depth-guided cross attention · 1.7controlnet · 1.7RGBD diffusion model · 1.73d mesh generation · 1.7
YearPublicationVenuePosition
2025 Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization
abstract
Exploring real-world spaces using novel-view synthesis is fun, and reimagining those worlds in a different style adds another layer of excitement. Stylized worlds can also be used for downstream tasks where there is limited training data and a need to expand a model’s training distribution. Most current novel-view synthesis stylization techniques lack the ability to convincingly change geometry. This is because editing geometry while maintaining multi-view consistency is very challenging. In this work, we propose a new autoregressive 3D Gaussian Splatting stylization method. As part of this method, we contribute a new RGBD diffusion model that allows for strength control over appearance and shape stylization. To ensure consistency across stylized frames, we use a combination of novel depth-guided cross attention, feature injection, and a ControlNet conditioned on composite frames for guiding the stylization of new frames. We validate our method via extensive qualitative results, quantitative experiments, and a user study.
Jamie Wynn, Zawar Qureshi, Jakub Powierza, Jamie Watson, Mohamed Sayed
CVPR3
2025 ImaginateAR: AI-Assisted In-Situ Authoring in Augmented Reality
abstract
While augmented reality (AR) enables new ways to play, tell stories, and explore ideas rooted in the physical world, authoring personalized AR content remains difficult for non-experts, often requiring professional tools and time. Prior systems have explored AI-driven XR design but typically rely on manually defined VR environments and fixed asset libraries, limiting creative flexibility and real-world relevance. We introduce ImaginateAR, the first mobile tool for AI-assisted AR authoring to combine offline scene understanding, fast 3D asset generation, and LLMs -- enabling users to create outdoor scenes through natural language interaction. For example, saying "a dragon enjoying a campfire" (P7) prompts the system to generate and arrange relevant assets, which can then be refined manually. Our technical evaluation shows that our custom pipelines produce more accurate outdoor scene graphs and generate 3D meshes faster than prior methods. A three-part user study (N=20) revealed preferred roles for AI, how users create in freeform use, and design implications for future AR authoring tools. ImaginateAR takes a step toward empowering anyone to create AR experiences anywhere -- simply by speaking their imagination.
Jaewook Lee 0005, Filippo Aleotti, Diego Mazala, Guillermo Garcia-Hernando, Sara Vicente, Oliver James Johnston, Isabel Kraus-Liang, Jakub Powierza, Jon Froehlich, Gabriel J. Brostow, Jessica Van Brummelen
UIST8