edit skeletondiffusion

Author: riccardomarin

_publications/skeletondiffusion/content.html

@@ -52,67 +52,12 @@ <h2 class="title is-3">Abstract</h2>
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   </section>
 
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-  <section class="section hero is-light">
-    <div class="container is-max-desktop">
-      <div class="columns is-centered has-text-centered">
-        <div class="column is-four-fifths">
-          <h2 class="title is-3">The Task</h2>
-          <div class="content has-text-justified">
-            <p>
-              In this work, we address the problem of predicting human motion based on observed past movements, known as Human Motion Prediction (HMP). Specifically, from a temporal sequence of human joint positions, we aim to forecast their evolution in subsequent frames.
-            </p>
-          </div>
-        </div>
-      </div>
-    </div>
-  </section>
-<img src="assets/method.png">
-  <section class="section hero is-light">
-    <div class="container is-max-desktop">
-      <div class="columns is-centered has-text-centered">
-        <div class="column is-four-fifths">
-          <h2 class="title is-3">Method Overview</h2>
-          <div class="content has-text-justified">
-            <p>
-We present SkeletonDiffusion, a latent diffusion model encoding this bias explicitly on both architecture and training.
-First, we consider the skeleton structure and joint categories throughout the entire network, and build our architecture end-to-end on top of Graph Convolutional Networks
-(GCNs). In contrast, existing SHMP approaches either ignore the skeleton’s graph structure or only
-leverage it at intermediate stage. 
-  <img src="assets/nonisodiff.png">
-
-  <br>
-Second, we replace the conventional isotropic
-Gaussian diffusion training with a novel nonisotropicformulation that accounts for joint relations directly in the
-generation process: the HMP problem is defined by the skeleton kinematic graph, and we exploit this knowledge to define a fixed non-diagonal noise covariance for the diffusion process
-
-            </p>
-          </div>
-        </div>
-      </div>
-    </div>
-  </section>
-
-<h2 class="title is-3">Results</h2>
   <section class="hero is-small">
     <div class="hero-body">
       <div class="container">
         <div class="columns is-centered has-text-centered">
           <div class="column is-four-fifths">
-            <div style="display: inline-block;text-align: center; margin:20px;">
-            <figure style="display: inline-block">
-            <img src="https://neural-icp.github.io/images/sample_2_input.gif" style="vertical-align: top;height:360;margin-bottom: -30px;">
-            <figcaption><b>Input</b></figcaption>
-            </figure>
-            <figure style="display: inline-block">
-            <img src="https://neural-icp.github.io/images/sample_2_smpl.gif" style="vertical-align:top;height:360;margin-bottom: -30px;">
-            <figcaption><b>Registration</b></figcaption>
-            </figure>
-            <figure style="display: inline-block">
-            <img src="https://neural-icp.github.io/images/sample_2_colors.gif" style="vertical-align:top;height:360;margin-bottom: -30px;">
-            <figcaption><b>Correspondences</b></figcaption>
-            </figure>
-            </div>
+            <h2 class="title is-3">Coming Soon</h2>
             <p class="content has-text-justified">
             </p>
           </div>