P
Patrick Pérez
Researcher at Valeo
Publications - 48
Citations - 2126
Patrick Pérez is an academic researcher from Valeo. The author has contributed to research in topics: Deep learning & Domain (software engineering). The author has an hindex of 16, co-authored 48 publications receiving 1499 citations.
Papers
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Proceedings ArticleDOI
Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz
Ayush Tewari,Michael Zollhöfer,Pablo Garrido,Florian Bernard,Hyeongwoo Kim,Patrick Pérez,Christian Theobalt +6 more
TL;DR: This first approach that jointly learns a regressor for face shape, expression, reflectance and illumination on the basis of a concurrently learned parametric face model is presented, which compares favorably to the state-of-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.
Journal ArticleDOI
Video Inpainting of Complex Scenes
TL;DR: In this article, an automatic video inpainting algorithm which relies on the optimization of a global, patch-based functional is proposed to deal with a variety of challenging situations, such as the correct reconstruction of dynamic textures, multiple moving objects, and moving background.
Proceedings ArticleDOI
DADA: Depth-Aware Domain Adaptation in Semantic Segmentation
TL;DR: This work proposes a unified depth-aware UDA framework that leverages in several complementary ways the knowledge of dense depth in the source domain, and achieves state-of-the-art performance on different challenging synthetic-2-real benchmarks.
Proceedings ArticleDOI
Automatic Face Reenactment
Pablo Garrido,Levi Valgaerts,Ole Rehmsen,Thorsten Thormaehlen,Patrick Pérez,Christian Theobalt +5 more
TL;DR: In this paper, an image-based, facial reenactment system was proposed to replace the face of an actor in an existing target video with a face of a user from a source video, while preserving the original performance.
Proceedings ArticleDOI
FML: Face Model Learning From Videos
Ayush Tewari,Florian Bernard,Pablo Garrido,Gaurav Bharaj,Mohamed Elgharib,Hans-Peter Seidel,Patrick Pérez,Michael Zollhöfer,Christian Theobalt +8 more
TL;DR: This work proposes multi-frame video-based self-supervised training of a deep network that learns a face identity model both in shape and appearance while jointly learning to reconstruct 3D faces.