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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.04233/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2405.04233/feed.xml" rel="self" type="application/rss+xml" /><item><title>Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models — HN submission</title><link>https://news.ycombinator.com/item?id=41379615</link><guid isPermaLink="false">urn:hackerpapers:submission:2405.04233:41379615</guid><pubDate>Wed, 28 Aug 2024 13:59:11 GMT</pubDate><description>&lt;p&gt;Show HN: Vidu – The New text/image-to-video MultiModal LLM&lt;/p&gt;&lt;p&gt;Hi HN, Vidu team here. We&amp;#x27;re excited to introduce our latest project, presented by Tsinghua University and Shengshu Technology - Vidu, a text and image-to-video MultiModal LLM. Professor Jun Zhu and his colleagues published a research paper on Vidu and its capabilities during Vidu&amp;#x27;s early stages: https://arxiv.org/abs/2405.04233 To set Vidu apart from the mass generative video AI that has already been released, our team chose to focus on augmenting specific properties during the early-stages of Vidu&amp;#x27;s developments, such as consistency. Inconsistencies has to be the most expressed concern…&lt;/p&gt;&lt;p&gt;1 point; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2405.04233/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
