Polywink - Automatic Expressions Blendshapes and Facial Rigs
3D animation in the blink of an eye Polywink is an online platform created to automate the most time-consuming and expensive aspects of facial animation. Thanks to our in-house procedural technology and through years of accumulated data powered by machine learning, we are able to complete in mere hours steps that would otherwise take weeks to achieve: from blendshapes generation to automatic facia
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「AI自動音声ã§ãŠä¼ãˆã—ã¦ã„ã¾ã™ã€ ã‚ã‚‹æœã®NHK「ãŠã¯ã‚ˆã†æ—¥æœ¬ã€ã®ç”»é¢ã€‚ãƒ‹ãƒ¥ãƒ¼ã‚¹æ˜ åƒã®å³ä¸Šã«ã€ã“ã‚“ãªæ–‡å—ãŒè¡¨ç¤ºã•ã‚Œã‚Œã¦ã„ã¦é©šã„ãŸã€‚音声ã«é•å’Œæ„ŸãŒãªãã€äººé–“ã®ã‚¢ãƒŠã‚¦ãƒ³ã‚µãƒ¼ã®å£°ã ã¨ã°ã‹ã‚Šæ€ã£ã¦ã„ãŸã‹ã‚‰ã 。 æ„è˜ã—ã¦ã‚ˆãèžã‘ã°ã€ã‚¤ãƒ³ãƒˆãƒãƒ¼ã‚·ãƒ§ãƒ³ã‚„“間â€ãŒã‚„ã‚„ä¸è‡ªç„¶ã ãŒã€æµã—èžãã—ã¦ã„ã‚‹ã ã‘ã§ã¯åˆ†ã‹ã‚‰ãªã„レベルã 。 å ´é¢ãŒã‚¹ã‚¿ã‚¸ã‚ªã«è»¢æ›ã™ã‚‹ã¨ã€äººé–“ã®ã‚¢ãƒŠã‚¦ãƒ³ã‚µãƒ¼ãŒ2人写ã£ãŸã€‚アナウンサーãŒãã“ã«ã„ã‚‹ã®ã«ã€AIãŒãƒ‹ãƒ¥ãƒ¼ã‚¹ã‚’èªã‚“ã§ã„ãŸã®ã 。 「ãŠã¯ã‚ˆã†æ—¥æœ¬ã€ã ã‘ã§ã¯ãªã„。NHKã¯ã€å¹³æ—¥åˆå¾Œã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ã€ãƒ©ã‚¸ã‚ªã€Webã®ãƒ‹ãƒ¥ãƒ¼ã‚¹ç•ªçµ„ã®ä¸€éƒ¨ãªã©ã§AIã«ã‚ˆã‚‹ã‚¢ãƒŠã‚¦ãƒ³ã‚¹ã‚’活用ã—ã¦ã„る。 AIアナウンスã¯ã©ã®ã‚ˆã†ã«ä½œã‚‰ã‚ŒãŸã®ã‹ï¼Ÿã€€äººé–“ã®ã‚¢ãƒŠãŒã„ã‚‹ã®ã«ã€ãªãœã‚ã–ã‚ã–ã€AIã«ãƒ‹ãƒ¥ãƒ¼ã‚¹ã‚’èªã‚“ã§ã‚‚らã†ã®ã‹ï¼Ÿã€€NHKã«èžã„ãŸã€‚ 放é€æ™‚é–“ã´ã£ãŸã‚Šã«ãƒ‹ãƒ¥ãƒ¼ã‚¹ãŒèªã‚ã‚‹AI AIアナウンスã®éŸ³å£°ã¯ã€NHKãŒé–‹ç™ºã—ãŸæŠ€è¡“ã 。気象情
Stanford CS 224N | Natural Language Processing with Deep Learning
Prerequisites Proficiency in Python All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine. C
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Innovative Tech： ã“ã®ã‚³ãƒ¼ãƒŠãƒ¼ã§ã¯ã€ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼ã®æœ€æ–°ç ”究を紹介ã™ã‚‹Webメディア「Seamlessã€ã‚’主宰ã™ã‚‹å±±ä¸‹è£•æ¯…æ°ãŒåŸ·ç†ã€‚æ–°è¦æ€§ã®é«˜ã„科å¦è«–文を山下æ°ãŒãƒ”ックアップã—ã€è§£èª¬ã™ã‚‹ã€‚ 米ワシントン大å¦ã®ç ”究ãƒãƒ¼ãƒ ãŒé–‹ç™ºã—ãŸã€ŒPeople as Scene Probesã€ã¯ã€ä¸€æ–¹å‘ã‹ã‚‰æ’®å½±ã—ãŸæ˜ åƒã®å¥¥è¡Œãã‚„å…‰ã®å½“ãŸã‚Šæ–¹ã‚’分æžã™ã‚‹æ·±å±¤å¦ç¿’ベースã®æŠ€è¡“ã 。ã“ã®æŠ€è¡“を使ã†ã“ã¨ã§ã€å½±ã‚„「オクルージョンã€ï¼ˆé®è”½ç‰©ã«ã‚ˆã£ã¦å…‰ã®åå°„ãŒã•ãˆãŽã‚‰ã‚Œã‚‹åº¦åˆã„ã€ç’°å¢ƒé®è”½ã¨ã‚‚） を考慮ã—ãŸãƒªã‚¢ãƒ«ãªç”»åƒåˆæˆãŒå¯èƒ½ã«ãªã‚‹ã€‚ æ˜ åƒå†…ã®ç‰©ä½“ã‹ã‚‰å¥¥è¡Œãã€ã‚ªã‚¯ãƒ«ãƒ¼ã‚¸ãƒ§ãƒ³ã€ç…§æ˜Žï¼å½±ã®æƒ…å ±ã‚’æŠ½å‡ºã—ã€æ–°ã—ã挿入ã™ã‚‹ç”»åƒã«é©å¿œã™ã‚‹ã“ã¨ã§è‡ªç„¶ãªåˆæˆç”»åƒã‚’生æˆã™ã‚‹ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®æ¦‚è¦å›³ã€‚下段ã®å›³ã§ã¯ã€æ‰‹å‰ã®äººç‰©ã‚„奥ã®èµ¤ã„車ã«å¯¾ã—ã¦é©åˆ‡ãªå½±ãŒåŠ ãˆã‚‰ã‚Œã¦ã„ã‚‹
People as Scene Probes
European Conference on Computer Vision (ECCV), 2020 We introduce a fully automatic pipeline for inferring depth, occlusion, and lighting/shadow information from image sequences of a scene. All theses information is extracted just by using people (and other objects such as cars) as scene probes to passively scan the scene. We also develop a tool for image compositing based on the inferred depth, oc
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新型コãƒãƒŠã‚¦ã‚£ãƒ«ã‚¹æ„ŸæŸ“拡大ã«ä¼´ã„ä¸è¦ã®å¤–出を控ãˆã‚‹ç¤¾ä¼šäººã‚„å¦ç”Ÿã®ã¿ãªã•ã¾ã«å‘ã‘〠JDLAèªå®šãƒ—ãƒã‚°ãƒ©ãƒ を実施ã™ã‚‹äº‹æ¥è€…ã®å”力を得ã¦ã€ä¸€éƒ¨ã®ã‚ªãƒ³ãƒ©ã‚¤ãƒ³å¦ç¿’コンテンツを期間é™å®šã§ç„¡æ–™å…¬é–‹ã„ãŸã—ã¾ã™ ＊2020å¹´1月20æ—¥ã«ã€ŒINTLOOPæ ªå¼ä¼šç¤¾ã€ã®è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊12月1æ—¥ã«ã€Œã‚¨ãƒƒã‚¸ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼æ ªå¼ä¼šç¤¾ã€ã®è¬›åº§æ供期間を延長ã—ã¾ã—㟠＊5月19æ—¥ã«ã€Œã‚¹ã‚ãƒ«ã‚¢ãƒƒãƒ—æ ªå¼ä¼šç¤¾ã€ã®è¬›åº§æœŸé–“定ã‚ãªã—ã€ç”³è¾¼ã¿æ–¹æ³•è¿½åŠ ã—ã¾ã—㟠＊5月13æ—¥ã«ã€Œã‚¹ã‚ãƒ«ã‚¢ãƒƒãƒ—æ ªå¼ä¼šç¤¾ã€ã®1è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊5月1æ—¥ã«ã€ŒStudy-AIæ ªå¼ä¼šç¤¾ã€ã®3è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊4月27æ—¥ã«ã€ŒNTTãƒ©ãƒ¼ãƒ‹ãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ã‚ºæ ªå¼ä¼šç¤¾ã€ã®1è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊4月24æ—¥ã«ã€Œæ ªå¼ä¼šç¤¾æ—¥çµŒBPã€ã®1è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊4月17æ—¥ã«ã€Œã‚¨ãƒƒã‚¸ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼æ ªå¼ä¼šç¤¾ã€ã®3è¬›åº§ã‚’è¿½åŠ ã—ã¾ã—㟠＊4月9æ—¥ã«ã€Œæ ªå¼ä¼šç¤¾ã‚カガクã€ã®1è¬›åº§ã‚’è¿½åŠ ã—
GitHub - microsoft/computervision-recipes: Best Practices, code samples, and documentation for Computer Vision.
In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity. This repository provides examples and best practice guidelines
Deep Tabnine - Tabnine Blog
Update (August 19): We’ve released Tabnine Local, which lets you run Deep Tabnine on your own machine. TL;DR: Tabnine is an autocompleter that helps you write code faster. We’re adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here. There has been a lot of hype about deep learning in the past few years. Neural network
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
https://blog.tensorflow.org/2019/05/introducing-tensorflow-graphics_9.html https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLtbFBddaXSQ9x-oBlhU_mUrk7nd9XpEr0bWXFRiE9lNjdeGKKf6Zl5DKbqn7eiaY032Wmck2JDliqalyJx_f43Y-4Mspjkz3X7eBze1o7NhSnsyDRvEIpHli9oqEFBuJshVeRt7cEmW02/s1600/tf+graphics.jpeg May 09, 2019 — Posted by Julien Valentin and Sofien Bouaziz Github repository: https://github.com/te
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Preferred Networksã¯4月10æ—¥ã€è‡ªç¤¾ã§é–‹ç™ºã™ã‚‹ã‚ªãƒ¼ãƒ—ンソースã®æ·±å±¤å¦ç¿’フレームワーク「Chainerã€ã«é–¢ã™ã‚‹æ—¥æœ¬èªžã®å¦ç¿’サイト「ディープラーニング入門：Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã€ã‚’無償公開ã—ãŸã€‚機械å¦ç¿’を勉強ã™ã‚‹ãŸã‚ã«å¿…è¦ãªæ•°å¦ã‚„統計å¦ã€ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªžPythonãªã©ã‚’基礎ã‹ã‚‰å¦ã¹ã‚‹ã¨ã„ã†ã€‚ 機械å¦ç¿’やディープラーニング（深層å¦ç¿’）ã®ä»•çµ„ã¿ã‚„使ã„方をç†è§£ã—ãŸã„大å¦ç”Ÿã‚„社会人å‘ã‘ã®ã‚ªãƒ³ãƒ©ã‚¤ãƒ³æ•™æを公開。大å¦ã®æŽˆæ¥ã€ä¼æ¥ã®ç ”ä¿®ã€å•†ç”¨ã‚»ãƒŸãƒŠãƒ¼ãªã©ã§èª°ã§ã‚‚ç„¡æ–™ã§ä½¿ç”¨ã§ãる。 サイト内ã§ã¯æ©Ÿæ¢°å¦ç¿’やディープラーニングã®åŸºç¤Žçš„ãªç†è«–を始ã‚ã€Pythonã®ä½¿ã„方やã€NumPyã€scikit-learnã€Pandasãªã©ã®ãƒ©ã‚¤ãƒ–ラリを用ã„ãŸå®Ÿè£…ã®ä»–ã€å¾®åˆ†ã€ç·šå½¢ä»£æ•°ã€ç¢ºçŽ‡ãƒ»çµ±è¨ˆãªã©ã‚‚å¦ã¹ã‚‹ã€‚ Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã§ã¯ã€ãƒ–ラウザ上ã§Pythonã®ã‚³ãƒ¼ãƒ‰ã‚’実行ã§ãã‚‹Goo
ディープラーニング入門：Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«
Chainer ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ« æ•°å¦ã®åŸºç¤Žã€ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°è¨€èªž Python ã®åŸºç¤Žã‹ã‚‰ã€æ©Ÿæ¢°å¦ç¿’・ディープラーニングã®ç†è«–ã®åŸºç¤Žã¨ã‚³ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã¾ã§ã‚’幅広ã解説 ※Chainerã®é–‹ç™ºã¯ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ãƒ¢ãƒ¼ãƒ‰ã«å…¥ã‚Šã¾ã—ãŸã€‚詳ã—ãã¯ã“ã¡ã‚‰ã‚’ã”覧ãã ã•ã„。 何ã‹ã‚‰å¦ã¶ã¹ãã‹è¿·ã‚ãªã„ ディープラーニングをå¦ã¶ã«ã¯ã€å¤§å¦ã§å¦ã¶ãƒ¬ãƒ™ãƒ«ã®æ•°å¦ã‚„ Python ã«ã‚ˆã‚‹ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®çŸ¥è˜ã«åŠ ãˆã¦ã€ Chainer ã®ã‚ˆã†ãªãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークã®ä½¿ã„æ–¹ã¾ã§ã€å¹…広ã„知è˜ãŒå¿…è¦ã¨ãªã‚Šã¾ã™ã€‚ 本ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€åˆå¦è€…ã«ã‚ˆãã‚る「ã¾ãšä½•ã‚’å¦ã¹ã°è‰¯ã„ã‹ã€ãŒåˆ†ã‹ã‚‰ãªã„〠ã¨ã„ã†å•é¡Œã‚’解決ã™ã‚‹ãŸã‚ã«è¨è¨ˆã•ã‚Œã¾ã—ãŸã€‚ åˆå¦è€…ã¯ã€Œã¾ãšä½•ã‚’ã€ãã—ã¦ã€Œæ¬¡ã«ä½•ã‚’ã€ã¨è¿·ã†ã“ã¨ãªãã€å¿…è¦ãªçŸ¥è˜ã‚’é †ç•ªã«å¦ç¿’ã§ãã¾ã™ã€‚ å‰æ知è˜ã‹ã‚‰è§£èª¬ ã“ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã¯ã€Chainer ãªã©ã®ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングフレームワークを使ã£ãŸãƒ—ãƒã‚°
Limitations of Deep Learning for Vision, and How We Might Fix Them
Limitations of Deep Learning for Vision, and How We Might Fix Them History of Deep Learning We are witnessing the third rise of deep learning. The first two waves — 1950s–1960s and 1980s–1990s — generated considerable excitement but slowly ran out of steam, since these neural networks neither achieved their promised performance gains nor aided our understanding of biological vision systems. The th
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TechCrunch | Startup and Technology News
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