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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Playing for Data: Ground Truth from Computer Games · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1608.02192/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1608.02192/feed.xml" rel="self" type="application/rss+xml" /><item><title>Playing for Data: Ground Truth from Computer Games — mentioned by bravura</title><link>https://news.ycombinator.com/item?id=26887746</link><guid isPermaLink="false">urn:hackerpapers:mention:1608.02192:26887746</guid><pubDate>Wed, 21 Apr 2021 10:43:23 GMT</pubDate><description>&lt;p&gt;This is important for vision ML because sufficiently advanced simulations may comprise the real world as a special case: https://arxiv.org/abs/1703.06907 For example, gaming pixel maps have been used for semantic simulation, and have been rendered scenes: https://arxiv.org/abs/1608.02192 https://www.cv-foundation.org/openaccess/content_cvpr_2016/p... This concept has not (yet) been applied in audio ML. We have a paper in submission---will be on ArXiv soon---where we share a GPU-enabled modular synthesizer that is 16000x faster than realtime, concurrently released with a 1-billion audio sample…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1608.02192/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
