4 citations · 5 across the 8 of their papers we have counts for
11 papers
STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models
Karol Dziekan, Przemysław Spurek, Dawid Malarz
Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image diffusion and assume a single gen…
LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
Kajetan Ożóg, Alicja Wojciechowska, Dawid Malarz +3
Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Language Models (LLMs) now face a…
From Unlearning to UNBRANDING: A Benchmark for Trademark-Safe Text-to-Image Generation
Dawid Malarz, Filip Manjak, Maciej Zięba +2
The rapid progress of text-to-image diffusion models raises significant concerns regarding the unauthorized reproduction of trademarked content. While prior work targets general co…
UnGuide: Learning to Forget with LoRA-Guided Diffusion Models
Agnieszka Polowczyk, Alicja Polowczyk, Dawid Malarz +4
Recent advances in large-scale text-to-image diffusion models have heightened concerns about their potential misuse, especially in generating harmful or misleading content. This un…
HuSc3D: Human Sculpture dataset for 3D object reconstruction
Weronika Smolak-Dyżewska, Dawid Malarz, Grzegorz Wilczyński +4
3D scene reconstruction from 2D images is one of the most important tasks in computer graphics. Unfortunately, existing datasets and benchmarks concentrate on idealized synthetic o…
Classifier-free Guidance with Adaptive Scaling
Dawid Malarz, Artur Kasymov, Maciej Zięba +2
Classifier-free guidance (CFG) is an essential mechanism in contemporary text-driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off b…
VeGaS: Video Gaussian Splatting
Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil +3
Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to tra…
Neural Surface Priors for Editable Gaussian Splatting
Jakub Szymkowiak, Weronika Jakubowska, Dawid Malarz +5
In computer graphics and vision, recovering easily modifiable scene appearance from image data is crucial for applications such as content creation. We introduce a novel method tha…
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz +3
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…
Gaussian Splatting with NeRF-based Color and Opacity
Dawid Malarz, Weronika Smolak, Jacek Tabor +2
Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of neural networks to capture the intricacies of 3D objects. By encoding the shape and color information w…