1.0Dynamic Implicit Neural Representations for Avatar Animationhttps://mscvprojects.ri.cmu.edu/2022team10rich600338<blockquote class="wp-embedded-content" data-secret="9WdQrWggZT"><a href="https://mscvprojects.ri.cmu.edu/2022team10/our-approach/">Spring ’22 Progress</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2022team10/our-approach/embed/#?secret=9WdQrWggZT" width="600" height="338" title="“Spring ’22 Progress” — Dynamic Implicit Neural Representations for Avatar Animation" data-secret="9WdQrWggZT" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://mscvprojects.ri.cmu.edu/2022team10/wp-includes/js/wp-embed.min.js </script> Challenges Since we are using MoFaNeRF as our backbone, there are some concerns we want to address. This method performs well on new data that fits the original training data distribution but fails when it encounters data that is much different. We want to increase the generalizability by augmenting it with a more diverse dataset. … Continue reading ""https://lh5.googleusercontent.com/pquHMw3sXrkEuFu_GmH0w1nqJEji4ziBGvt1Vqp2DaMizn_hU8hz2ocp36Ljd5qA6oqXcd0a9pDhXTqz1ljlW8fPbCHDPfHzKf1JZvQpsYcZrp6EztRVOZoU14QlcR-WKLKzEI7bgh0o