{"id":38674,"date":"2019-10-31T22:25:15","date_gmt":"2019-10-31T19:25:15","guid":{"rendered":"https:\/\/prohoster.info\/blog\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej\/"},"modified":"2019-10-31T22:25:15","modified_gmt":"2019-10-31T19:25:15","slug":"otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/otkryt-kod-dlya-sinteza-animatsii-s-pomoshhyu-nejrosetej","title":{"rendered":"Il codice per la sintesi di animazioni tramite reti neurali \u00e8 ora aperto.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un gruppo di ricercatori dell'Universit\u00e0 Tecnica di Shanghai. ha pubblicato<\/a><\/noindex> strumentazione Impersonator<\/a><\/noindex>, che utilizza metodi di apprendimento automatico per simulare i movimenti delle persone su immagini statiche, sostituire i vestiti, spostare l'oggetto in un altro contesto e cambiare l'angolazione da cui \u00e8 visibile. Il codice \u00e8 scritto in Python.
\nutilizzando il framework PyTorch<\/a><\/noindex>. Per la compilazione \u00e8 necessario anche torchvision<\/a><\/noindex> e CUDA Toolkit. <\/p>\n

\"Il<\/center><\/p>\n

L'strumentazione riceve in ingresso un'immagine bidimensionale e sintetizza il risultato modificato sulla base del modello selezionato. Sono supportate tre varianti di trasformazioni:
\nCreazione di un oggetto in movimento che replica i movimenti su cui \u00e8 stato addestrato il modello. Trasferimento di elementi estetici dal modello all'oggetto (ad esempio, cambio di abbigliamento). Generazione di una nuova angolazione (ad esempio, sintesi di un'immagine di profilo basata su una foto frontale). Tutti e tre i metodi possono essere combinati; per esempio, \u00e8 possibile generare un video da una foto che imita l'esecuzione di un acrobatico complesso in abiti diversi. <\/p>\n

\r\n \"Riproduci\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

Durante il processo di sintesi vengono eseguite operazioni di estrazione dell'oggetto nella fotografia e di ricostruzione degli elementi di sfondo mancanti con il movimento. Il modello per la rete neurale pu\u00f2 essere addestrato una sola volta e utilizzato per diverse trasformazioni. Per il caricamento sono disponibili<\/a><\/noindex> modelli gi\u00e0 pronti, che consentono di utilizzare subito gli strumenti senza dover effettuare un allenamento preliminare. \u00c8 richiesta una GPU con almeno 8GB di memoria.<\/p>\n

A differenza dei metodi di trasformazione basati su punti chiave che descrivono la posizione del corpo in uno spazio bidimensionale, in Impersonator \u00e8 stato effettuato un tentativo di sintesi di una maglia tridimensionale (mesh) descrivente il corpo, utilizzando metodi di apprendimento automatico.
\nIl metodo proposto consente di effettuare manipolazioni tenendo conto della forma corporeo personalizzata e della posizione attuale, simulando i movimenti naturali degli arti. <\/p>\n

\"Il<\/a><\/noindex><\/center><\/p>\n

Per mantenere le informazioni originali, come texture, stile, colori e riconoscibilit\u00e0 del volto, durante il processo di trasformazione viene utilizzata una rete neurale generativa avversaria<\/a><\/noindex> (Liquid Warping GAN<\/a><\/noindex>). Le informazioni sullo stato originale e i parametri per la sua precisa identificazione vengono estratte attraverso l'applicazione di una rete neurale convoluzionale<\/a><\/noindex>.<\/p>\n

\r\n \"Riproduci\r\n
<\/div>\r\n <\/div>
\n<\/center><\/p>\n

<\/a><\/noindex><\/p>\n

Fonte: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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Il codice \u00e8 scritto in Python utilizzando il framework PyTorch. \u00c8 richiesta anche l'installazione di torchvision e CUDA Toolkit. 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