Autoregressive GAN for Semantic Unconditional Head Motion Generation

by Louis Airale, Xavier Alameda-Pineda, Stéphane Lathuilière, and Dominique Vaufreydaz ACM Transactions on Multimedia Tools and Applications [paper][code] Abstract: We address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space. Deviating from talking head generation conditioned on audio that seldom emphasizes realistic…

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Unsupervised Performance Analysis of 3D Face Alignment with a Statistically Robust Confidence Test

by Mostafa Sadeghi,  Xavier Alameda-Pineda and Radu Horaud Neurocomputing, volume 564, January 2024 [Code & Data] Abstract: We address the problem of analyzing the performance of 3D face alignment (3DFA), or facial landmark localization. Performance analysis is usually based on annotated datasets. Nevertheless, in the particular case of 3DFA, the…

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Motion-DVAE: Unsupervised learning for fast human motion denoising

by Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, and Renaud Séguier ACM SIGGRAPH Conference on Motion, Interaction and Games [paper][code] Abstract: Pose and motion priors are crucial for recovering realistic and accurate human motion from noisy observations. Substantial progress has been made on pose and shape estimation from images, and recent…

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On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers

by Thomas De Min, Massimiliano Mancini, Karteek Alahari, Xavier Alameda-Pineda, and Elisa Ricci ICCV 2023 Workshops [paper][code] Abstract: State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forgetting. However, there is a tradeoff between the number of learned parameters and the…

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A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation

by Louis Airale, Dominique Vaufreydaz, and Xavier Alameda-Pineda [paper][code] Abstract: Animating still face images with deep generative models using a speech input signal is an active research topic and has seen important recent progress. However, much of the effort has been put into lip syncing and rendering quality while the…

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Semi-supervised learning made simple with self-supervised clustering

by Enrico Fini, Pietro Astolfi, Karteek Alahari, Xavier Alameda-Pineda, Julien Mairal, Moin Nabi, and Elisa Ricci IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 [paper][code] Abstract: Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially…

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Back to MLP: A Simple Baseline for Human Motion Prediction

by Wen Guo*, Yuming Du*, Xi Shen, Vincent Lepetit, Xavier Alameda-Pineda, and Francesc Moreno-Noguer IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023, Waikoloa, Hawaii [paper] [code] [HAL] Abstract. This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art…

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Expression-preserving face frontalization improves visually assisted speech processing

by Zhiqi Kang, Mostafa Sadeghi, Radu Horaud and Xavier Alameda-Pineda International Journal of Computer Vision, 2023, 131 (5), pp.1122-1140   [arXiv] [HAL] [webpage] Abstract. Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a frontalization methodology that preserves non-rigid facial deformations in order to boost…

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Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding,  Hao Tang, Xavier Alameda-Pineda, Elisa Ricci IEEE Transactions on Multimedia [arXiv][HAL] Abstract. Over the past years, semantic segmentation, similar to many other tasks in computer vision, has benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained…

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