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@@ -200,6 +200,7 @@ Marie-Lannelongue</a><sup>2</sup>
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       <img src="./static/images/PARTAGES BASELINE_RVB.png" alt="Project logo" style="max-height: 120px;">
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@@ -207,31 +208,10 @@ Marie-Lannelongue</a><sup>2</sup>
         <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.
+            PARTAGES (Advanced Development of Digital Commons for Generative Artificial Intelligence in Healthcare) is a project coordinated by the Health Data Hub. A winner of the “Digital Commons for Generative AI” call for projects under the France 2030 plan, it aims to accelerate and democratize the use of large language models (LLMs) for healthcare professionals.
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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.
-          </p>
-          <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.
+            Its goal: to create a national momentum fostering the emergence of open generative AI solutions in healthcare, as well as their adoption within the healthcare ecosystem—whether academic, research-based, or industrial.
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