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Depth-based hand pose estimation : Methods, data, and challenges / James Steven Supančič in International journal of computer vision, vol 126 n° 11 (November 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 11 (November 2018) . - pp 1180 - 1198 Titre : Depth-based hand pose estimation : Methods, data, and challenges Type de document : Article/Communication Auteurs : James Steven Supančič, Auteur ; Grégory Rogez, Auteur ; Yi Yang, Auteur ; Jamie Shotton, Auteur ; Deva Ramanan, Auteur Année de publication : 2018 Article en page(s) : pp 1180 - 1198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] estimation de pose [Termes IGN] état de l'art [Termes IGN] image RVB [Termes IGN] plus proche voisin, algorithme du Résumé : (Auteur) Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress. Numéro de notice : A2018-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1081-7 Date de publication en ligne : 12/04/2018 En ligne : https://doi.org/10.1007/s11263-018-1081-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92523 [article]

Image-based synthesis for deep 3D human pose estimation / Grégory Rogez in International journal of computer vision, vol 126 n° 9 (September 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 9 (September 2018) . - pp 993 - 1008 Titre : Image-based synthesis for deep 3D human pose estimation Type de document : Article/Communication Auteurs : Grégory Rogez, Auteur ; Cordelia Schmid, Auteur Année de publication : 2018 Article en page(s) : pp 993 - 1008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] données localisées 3D [Termes IGN] estimation de pose [Termes IGN] réseau neuronal convolutif [Termes IGN] synthèse d'image Résumé : (Auteur) This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D motion capture data. Given a candidate 3D pose, our algorithm selects for each joint an image whose 2D pose locally matches the projected 3D pose. The selected images are then combined to generate a new synthetic image by stitching local image patches in a kinematically constrained manner. The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation. We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem. Such an approach is viable only with large training sets such as ours. Our method outperforms most of the published works in terms of 3D pose estimation in controlled environments (Human3.6M) and shows promising results for real-world images (LSP). This demonstrates that CNNs trained on artificial images generalize well to real images. Compared to data generated from more classical rendering engines, our synthetic images do not require any domain adaptation or fine-tuning stage. Numéro de notice : A2018-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1071-9 Date de publication en ligne : 19/03/2018 En ligne : https://doi.org/10.1007/s11263-018-1071-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90901 [article]