update 21:13:48 03-04-2024

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         <h2 class="title is-3">Abstract</h2>
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+            We present, PEGASUS, a method for constructing personalized generative 3D face avatars from monocular video
+            sources. 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. 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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