2 citations · 3 across the 9 of their papers we have counts for
11 papers
CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork
Wojciech Gromski, Patryk Krukowski, Jan Miksa +2
Continual personalization of text-to-image diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. However, existing methods either s…
ORCA: Occlusion-Aware Refinement and Completion for Novel View Synthesis
Weronika Jakubowska, Maciej Zięba, Przemysław Spurek
Novel-view synthesis from a single image is a fundamentally ambiguous problem. As the camera moves away from the input viewpoint, previously hidden regions become visible, exposing…
UnHype: CLIP-Guided Hypernetworks for Dynamic LoRA Unlearning
Piotr Wójcik, Maksym Petrenko, Wojciech Gromski +2
Recent advances in large-scale diffusion models have intensified concerns about their potential misuse, particularly in generating realistic yet harmful or socially disruptive cont…
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…
GaINeR: Geometry-Aware Implicit Network Representation
Weronika Jakubowska, Mikołaj Zieliński, Rafał Tobiasz +4
Implicit Neural Representations (INRs) are widely used for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Architectures su…
KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation
Antoni Bigata, Michał Stypułkowski, Rodrigo Mira +7
Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. E…
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…
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…
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
Artur Kasymov, Marcin Sendera, Michał Stypułkowski +2
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the…
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming
Wojciech Kozłowski, Michał Szachniewicz, Michał Stypułkowski +1
Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations and limited access to photos with ground-truth lighting con…
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model
Dan Bigioi, Shubhajit Basak, Michał Stypułkowski +4
Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diff…