Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Tzelepis, Christos"'
Generating human portraits is a hot topic in the image generation area, e.g. mask-to-face generation and text-to-face generation. However, these unimodal generation methods lack controllability in image generation. Controllability can be enhanced by
Externí odkaz:
http://arxiv.org/abs/2409.11010
The steady improvement of Diffusion Models for visual synthesis has given rise to many new and interesting use cases of synthetic images but also has raised concerns about their potential abuse, which poses significant societal threats. To address th
Externí odkaz:
http://arxiv.org/abs/2408.09153
Autor:
Marimont, Sergio Naval, Siomos, Vasilis, Baugh, Matthew, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by gener
Externí odkaz:
http://arxiv.org/abs/2407.06635
Autor:
Bounareli, Stella, Tzelepis, Christos, Argyriou, Vasileios, Patras, Ioannis, Tzimiropoulos, Georgios
Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face, while transferring the target head pose and facial expressions. Existing GAN-based methods suffer
Externí odkaz:
http://arxiv.org/abs/2403.17217
Autor:
Oldfield, James, Georgopoulos, Markos, Chrysos, Grigorios G., Tzelepis, Christos, Panagakis, Yannis, Nicolaou, Mihalis A., Deng, Jiankang, Patras, Ioannis
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability. However, a major challenge lies in the computational c
Externí odkaz:
http://arxiv.org/abs/2402.12550
Autor:
Bounareli, Stella, Tzelepis, Christos, Argyriou, Vasileios, Patras, Ioannis, Tzimiropoulos, Georgios
In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head p
Externí odkaz:
http://arxiv.org/abs/2402.03553
Autor:
Marimont, Sergio Naval, Baugh, Matthew, Siomos, Vasilis, Tzelepis, Christos, Kainz, Bernhard, Tarroni, Giacomo
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free of anomalies. Diffusion models learn to modify inputs $x$ to increase
Externí odkaz:
http://arxiv.org/abs/2311.15453
Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin color) with unrelated attributes (downstream tasks), resulting in biases
Externí odkaz:
http://arxiv.org/abs/2311.01573
Autor:
Bounareli, Stella, Tzelepis, Christos, Argyriou, Vasileios, Patras, Ioannis, Tzimiropoulos, Georgios
In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train
Externí odkaz:
http://arxiv.org/abs/2307.10797
Autor:
Oldfield, James, Tzelepis, Christos, Panagakis, Yannis, Nicolaou, Mihalis A., Patras, Ioannis
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks. However, their utility is limited by their entanglement with respect to different visual attributes. For instance, recent
Externí odkaz:
http://arxiv.org/abs/2305.14053