16 citations · 18 across the 9 of their papers we have counts for
10 papers
FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs
Andreas Zinonos, Michał Stypułkowski, Antoni Bigata +3
We present FlashLips, a two-stage, mask-free lip-sync system that decouples lips control from rendering and achieves real-time performance, with our U-Net variant running at over 1…
KeySync: A Robust Approach for Leakage-free Lip Synchronization in High Resolution
Antoni Bigata, Rodrigo Mira, Stella Bounareli +4
Lip synchronization, known as the task of aligning lip movements in an existing video with new input audio, is typically framed as a simpler variant of audio-driven facial animatio…
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…
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…
Self-supervised adversarial masking for 3D point cloud representation learning
Michał Szachniewicz, Wojciech Kozłowski, Michał Stypułkowski +1
Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of…
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Michał Stypułkowski, Konstantinos Vougioukas, Sen He +3
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments i…
Continual learning on 3D point clouds with random compressed rehearsal
Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski +2
Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype…
Representing Point Clouds with Generative Conditional Invertible Flow Networks
Michał Stypułkowski, Kacper Kania, Maciej Zamorski +3
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation match…
Conditional Invertible Flow for Point Cloud Generation
Michał Stypułkowski, Maciej Zamorski, Maciej Zięba +1
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a pro…