update 18:54:07 02-29-2024

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@@ -162,7 +162,7 @@ <h1 class="title is-1 publication-title">PEGASUS: Personalized Generative 3D Ava
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         <h2 class="title is-3">Abstract</h2>
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           <p>
-            We present the first method capable of photorealistically reconstructing a non-rigidly
-            deforming scene using photos/videos captured casually from mobile phones.
+            We present, <i>"PEGASUS"</i>, a method for constructing personalized generative 3D face avatars from monocular video sources.
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           <p>
-            Our approach augments neural radiance fields
-            (NeRF) by optimizing an
-            additional continuous volumetric deformation field that warps each observed point into a
-            canonical 5D NeRF.
-            We observe that these NeRF-like deformation fields are prone to local minima, and
-            propose a coarse-to-fine optimization method for coordinate-based models that allows for
-            more robust optimization.
-            By adapting principles from geometry processing and physical simulation to NeRF-like
-            models, we propose an elastic regularization of the deformation field that further
-            improves robustness.
+            As a compositional generative model, 
+            our model enables disentangled controls to selectively alter the facial attributes (e.g., hair or nose) of the target individual, 
+            while preserving the identity. We present two key approaches to achieve this goal.
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           <p>
-            We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
-            photos/videos into deformable NeRF
-            models that allow for photorealistic renderings of the subject from arbitrary
-            viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
-            using a
-            rig with two mobile phones that take time-synchronized photos, yielding train/validation
-            images of the same pose at different viewpoints. We show that our method faithfully
-            reconstructs non-rigidly deforming scenes and reproduces unseen views with high
-            fidelity.
+            First, we present a method to construct a person-specific generative 3D avatar 
+            by building a synthetic video collection of the target identity with varying facial attributes, 
+            where the videos are synthesized by borrowing parts from diverse individuals from other monocular videos. 
+            Through several experiments, we demonstrate the superior performance of our approach by generating unseen attributes with high realism. 
+            Subsequently, we introduce a zero-shot approach to achieve the same generative modeling more efficiently 
+            by leveraging a previously constructed personalized generative model. 
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