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<oembed><version>1.0</version><provider_name>Dynamic Implicit Neural Representations for Avatar Animation</provider_name><provider_url>https://mscvprojects.ri.cmu.edu/2022team10</provider_url><title>Background - Dynamic Implicit Neural Representations for Avatar Animation</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="QuoLVC8WTy"&gt;&lt;a href="https://mscvprojects.ri.cmu.edu/2022team10/spring-2020/"&gt;Background&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2022team10/spring-2020/embed/#?secret=QuoLVC8WTy" width="600" height="338" title="&#x201C;Background&#x201D; &#x2014; Dynamic Implicit Neural Representations for Avatar Animation" data-secret="QuoLVC8WTy" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Before diving straight into our approach we would like to introduce some key components we will be going back to throughout our experiments. Previous Works NeRF Recently there has been an explosion in NeRF or Neural Radiance Field [1] which explores novel-view synthesis by using a sparse set of 100 input images. This gained popularity &hellip; Continue reading ""</description><thumbnail_url>http://mscvprojects.ri.cmu.edu/2022team10/wp-content/uploads/sites/65/2022/04/lego.gif</thumbnail_url></oembed>

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