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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Material Editing Using a Physically Based Rendering Network · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1708.00106/</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/1708.00106/feed.xml" rel="self" type="application/rss+xml" /><item><title>Material Editing Using a Physically Based Rendering Network — mentioned by alexbeloi</title><link>https://news.ycombinator.com/item?id=15598578</link><guid isPermaLink="false">urn:hackerpapers:mention:1708.00106:15598578</guid><pubDate>Wed, 01 Nov 2017 03:15:35 GMT</pubDate><description>&lt;p&gt;I spoke in hyperbole, there were dozens of papers using generative networks to create real looking simulated data which is the more common approach. You start with noise + some features you want in your image, then you turn that into a realistic image. The notable exception is if you want to generate segmentation data (where you want labels for each pixel), in this case the set of features you want is essentially an image already. ICCV 2017 * Photographic Image Synthesis with Cascaded Refinement Networks: https://arxiv.org/pdf/1707.09405.pdf * Synthetic avatars…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1708.00106/"&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>
