EG 2022 - Posters

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https://diglib.eg.org/handle/10.2312/2633166

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Browsing EG 2022 - Posters by Author "Loscos, Celine"

Now showing 1 - 3 of 3
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    3D Human Shape and Pose from a Single Depth Image with Deep Dense Correspondence Enabled Model Fitting
    (The Eurographics Association, 2022) Wang, Xiaofang; Boukhayma, Adnane; Prévost, Stéphanie; Desjardin, Eric; Loscos, Celine; Multon, Franck; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We propose a two-stage hybrid method, with no initialization, for 3D human shape and pose estimation from a single depth image, combining the benefits of deep learning and optimization. First, a convolutional neural network predicts pixel-wise dense semantic correspondences to a template geometry, in the form of body part segmentation labels and normalized canonical geometry vertex coordinates. Using these two outputs, pixel-to-vertex correspondences are computed in a six-dimensional embedding of the template geometry through nearest neighbor. Second, a parametric shape model (SMPL) is fitted to the depth data by minimizing vertex distances to the input. Extensive evaluation on both real and synthetic human shape in motion datasets shows that our method yields quantitatively and qualitatively satisfactory results and state-of-the-art reconstruction errors.
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    Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
    (The Eurographics Association, 2022) Mohan, Aditya; Zhang, Jing; Cozot, Rémi; Loscos, Celine; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We propose a CNN-based approach for reconstructing HDR images from just a single exposure. It predicts the saturated areas of LDR images and then blends the linearized input with the predicted outputs. Two loss functions are used: the Mean Absolute Error and the Multi-Scale Structural Similarity Index. The choice of these loss functions allows us to outperform previous algorithms in the reconstructed dynamic range. Once the network trained, we input multi-view images to it to output multi-view coherent images.
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    Fast and Fine Disparity Reconstruction for Wide-baseline Camera Arrays with Deep Neural Networks
    (The Eurographics Association, 2022) Barrios, Théo; Gerhards, Julien; Prévost, Stéphanie; Loscos, Celine; Sauvage, Basile; Hasic-Telalovic, Jasminka
    Recently, disparity-based 3D reconstruction for stereo camera pairs and light field cameras have been greatly improved with the uprising of deep learning-based methods. However, only few of these approaches address wide-baseline camera arrays which require specific solutions. In this paper, we introduce a deep-learning based pipeline for multi-view disparity inference from images of a wide-baseline camera array. The network builds a low-resolution disparity map and retains the original resolution with an additional up scaling step. Our solution successfully answers to wide-baseline array configurations and infers disparity for full HD images at interactive times, while reducing quantification error compared to the state of the art.