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@@ -3,10 +3,10 @@
 <head>
   <meta charset="utf-8">
   <meta name="description"
-        content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
-  <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>Nerfies: Deformable Neural Radiance Fields</title>
   <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
         rel="stylesheet">
@@ -33,68 +33,22 @@
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
-          <h1 class="title is-1 publication-title">Nerfies: Deformable Neural Radiance Fields</h1>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
-              <a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
-            <span class="author-block">
-              <a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
-            <span class="author-block">
-              <a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
             </span>
           </div>
           <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>University of Washington,</span>
-            <span class="author-block"><sup>2</sup>Google Research</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
-              <!-- PDF Link. -->
-              <span class="link-block">
-                <a href="https://arxiv.org/pdf/2011.12948" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="fas fa-file-pdf"></i>
-                  </span>
-                  <span>Paper</span>
-                </a>
-              </span>
-              <span class="link-block">
-                <a href="https://arxiv.org/abs/2011.12948" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="ai ai-arxiv"></i>
-                  </span>
-                  <span>arXiv</span>
-                </a>
-              </span>
-              <!-- Video Link. -->
-              <span class="link-block">
-                <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="fab fa-youtube"></i>
-                  </span>
-                  <span>Video</span>
-                </a>
-              </span>
               <!-- Code Link. -->
               <span class="link-block">
-                <a href="https://github.com/google/nerfies" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fab fa-github"></i>
@@ -102,17 +56,7 @@
                   <span>Code</span>
                   </a>
               </span>
-              <!-- Dataset Link. -->
-              <span class="link-block">
-                <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="far fa-images"></i>
-                  </span>
-                  <span>Data</span>
-                  </a>
             </div>
           </div>
         </div>
       </div>
@@ -123,14 +67,10 @@
 <section class="hero teaser">
   <div class="container is-max-desktop">
     <div class="hero-body">
-      <video id="teaser" autoplay muted loop playsinline height="100%">
-        <source src="./static/videos/teaser.mp4"
-                type="video/mp4">
-      </video>
-      <h2 class="subtitle has-text-centered">
-        <span class="dnerf">Nerfies</span> turns selfie videos from your phone into
-        free-viewpoint
-        portraits.
       </h2>
     </div>
   </div>
@@ -141,53 +81,35 @@
   <div class="hero-body">
     <div class="container">
       <div id="results-carousel" class="carousel results-carousel">
-        <div class="item item-steve">
-          <video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/steve.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-chair-tp">
-          <video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/chair-tp.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-shiba">
-          <video poster="" id="shiba" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/shiba.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-fullbody">
-          <video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/fullbody.mp4"
-                    type="video/mp4">
-          </video>
         </div>
-        <div class="item item-blueshirt">
-          <video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/blueshirt.mp4"
-                    type="video/mp4">
-          </video>
         </div>
-        <div class="item item-mask">
-          <video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/mask.mp4"
-                    type="video/mp4">
-          </video>
         </div>
-        <div class="item item-coffee">
-          <video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/coffee.mp4"
-                    type="video/mp4">
-          </video>
         </div>
-        <div class="item item-toby">
-          <video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/toby2.mp4"
-                    type="video/mp4">
-          </video>
         </div>
       </div>
     </div>
@@ -197,37 +119,20 @@
 <section class="section">
   <div class="container is-max-desktop">
-    <!-- Abstract. -->
     <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
-        <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
-            We present the first method capable of photorealistically reconstructing a non-rigidly
-            deforming scene using photos/videos captured casually from mobile phones.
           </p>
-          <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.
           </p>
         </div>
       </div>
@@ -237,10 +142,17 @@
     <!-- Paper video. -->
     <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
-        <h2 class="title is-3">Video</h2>
-        <div class="publication-video">
-          <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0"
-                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
         </div>
       </div>
     </div>
@@ -253,160 +165,63 @@
   <div class="container is-max-desktop">
     <div class="columns is-centered">
-      <!-- Visual Effects. -->
       <div class="column">
         <div class="content">
-          <h2 class="title is-3">Visual Effects</h2>
-          <p>
-            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
-            would be impossible without nerfies since it would require going through a wall.
-          </p>
-          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/dollyzoom-stacked.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-      </div>
-      <!--/ Visual Effects. -->
-      <!-- Matting. -->
-      <div class="column">
-        <h2 class="title is-3">Matting</h2>
-        <div class="columns is-centered">
-          <div class="column content">
-            <p>
-              As a byproduct of our method, we can also solve the matting problem by ignoring
-              samples that fall outside of a bounding box during rendering.
-            </p>
-            <video id="matting-video" controls playsinline height="100%">
-              <source src="./static/videos/matting.mp4"
-                      type="video/mp4">
-            </video>
-          </div>
         </div>
       </div>
     </div>
-    <!--/ Matting. -->
-    <!-- Animation. -->
     <div class="columns is-centered">
       <div class="column is-full-width">
-        <h2 class="title is-3">Animation</h2>
-        <!-- Interpolating. -->
-        <h3 class="title is-4">Interpolating states</h3>
         <div class="content has-text-justified">
           <p>
-            We can also animate the scene by interpolating the deformation latent codes of two input
-            frames. Use the slider here to linearly interpolate between the left frame and the right
-            frame.
           </p>
         </div>
-        <div class="columns is-vcentered interpolation-panel">
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_start.jpg"
-                 class="interpolation-image"
-                 alt="Interpolate start reference image."/>
-            <p>Start Frame</p>
-          </div>
-          <div class="column interpolation-video-column">
-            <div id="interpolation-image-wrapper">
-              Loading...
-            </div>
-            <input class="slider is-fullwidth is-large is-info"
-                   id="interpolation-slider"
-                   step="1" min="0" max="100" value="0" type="range">
-          </div>
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_end.jpg"
-                 class="interpolation-image"
-                 alt="Interpolation end reference image."/>
-            <p class="is-bold">End Frame</p>
-          </div>
         </div>
-        <br/>
-        <!--/ Interpolating. -->
-        <!-- Re-rendering. -->
-        <h3 class="title is-4">Re-rendering the input video</h3>
         <div class="content has-text-justified">
           <p>
-            Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
-            viewpoint such as a stabilized camera by playing back the training deformations.
           </p>
         </div>
-        <div class="content has-text-centered">
-          <video id="replay-video"
-                 controls
-                 muted
-                 preload
-                 playsinline
-                 width="75%">
-            <source src="./static/videos/replay.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <!--/ Re-rendering. -->
-      </div>
-    </div>
-    <!--/ Animation. -->
-    <!-- Concurrent Work. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Related Links</h2>
         <div class="content has-text-justified">
           <p>
-            There's a lot of excellent work that was introduced around the same time as ours.
-          </p>
-          <p>
-            <a href="https://arxiv.org/abs/2104.09125" target="_blank">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
-          </p>
-          <p>
-            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
-            both use deformation fields to model non-rigid scenes.
-          </p>
-          <p>
-            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
-          </p>
-          <p>
-            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
           </p>
         </div>
       </div>
     </div>
-    <!--/ Concurrent Work. -->
   </div>
 </section>
-<section class="section" id="BibTeX">
-  <div class="container is-max-desktop content">
-    <h2 class="title">BibTeX</h2>
-    <pre><code>@article{park2021nerfies,
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}</code></pre>
-  </div>
-</section>
 <footer class="footer">
   <div class="container">
     <div class="content has-text-centered">
-      <a class="icon-link" target="_blank"
-         href="./static/videos/nerfies_paper.pdf">
-        <i class="fas fa-file-pdf"></i>
-      </a>
-      <a class="icon-link" href="https://github.com/keunhong" target="_blank" class="external-link" disabled>
         <i class="fab fa-github"></i>
       </a>
     </div>
@@ -419,11 +234,7 @@
             Commons Attribution-ShareAlike 4.0 International License</a>.
           </p>
           <p>
-            This means you are free to borrow the <a target="_blank"
-              href="https://github.com/nerfies/nerfies.github.io">source code</a> of this website,
-            we just ask that you link back to this page in the footer.
-            Please remember to remove the analytics code included in the header of the website which
-            you do not want on your website.
           </p>
         </div>
       </div>
@@ -432,4 +243,4 @@
 </footer>
 </body>
-</html>

 <head>
   <meta charset="utf-8">
   <meta name="description"
+        content="MPCA is a novel, bio-inspired AI framework that moves beyond traditional machine learning models.">
+  <meta name="keywords" content="MPCA, MycoPhysarum, Cognitive Architecture, Bio-inspired AI, Slime Mold, Mycelium, Graph AI, Efficient AI">
   <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>MycoPhysarum Cognitive Architecture (MPCA)</title>
   <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
         rel="stylesheet">
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
+          <h1 class="title is-1 publication-title">MycoPhysarum Cognitive Architecture (MPCA)</h1>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
+              A Novel, Bio-Inspired AI Framework
             </span>
           </div>
           <div class="is-size-5 publication-authors">
+            <span class="author-block">Inspired by the decentralized intelligence of slime molds and mycorrhizal networks.</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
               <!-- Code Link. -->
               <span class="link-block">
+                <a href="https://github.com/mlabonne/myco-physarum" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fab fa-github"></i>
                   <span>Code</span>
                   </a>
               </span>
             </div>
           </div>
         </div>
       </div>
 <section class="hero teaser">
   <div class="container is-max-desktop">
     <div class="hero-body">
+      <!-- Replaced video with a conceptual diagram -->
+      <img src="https://huggingface.co/mlabonne/myco-physarum/resolve/main/mpca.png" alt="MPCA Architecture Diagram" style="width: 100%; max-width: 800px; margin: auto; display: block; border-radius: 10px;">
+      <h2 class="subtitle has-text-centered" style="margin-top: 2rem;">
+        MPCA represents knowledge not as static data, but as a dynamic, living graph that evolves through interaction and self-reflection.
       </h2>
     </div>
   </div>
   <div class="hero-body">
     <div class="container">
       <div id="results-carousel" class="carousel results-carousel">
+        <div class="item">
+          <div class="content" style="padding: 2rem;">
+            <h3 class="title is-4">Mycelium</h3>
+            <p>The heart of the system. A <code>networkx</code> directed graph where nodes are concepts and edges represent the relationships between them. The strength of these connections is dynamic, changing based on usage and learning.</p>
+          </div>
         </div>
+        <div class="item">
+           <div class="content" style="padding: 2rem;">
+            <h3 class="title is-4">Builder</h3>
+            <p>The architect of the Mycelium. The Builder ingests raw text, uses <code>spaCy</code>'s dependency parser to understand grammatical structure, and translates it into a rich graph of nodes and relationships.</p>
+          </div>
         </div>
+        <div class="item">
+           <div class="content" style="padding: 2rem;">
+            <h3 class="title is-4">Solver</h3>
+            <p>The "consciousness" of MPCA. It traverses the Mycelium to find relevant pathways to answer queries. Implements Hebbian learning to reinforce successful paths and "dreaming" to form new speculative connections.</p>
+          </div>
         </div>
+        <div class="item">
+           <div class="content" style="padding: 2rem;">
+            <h3 class="title is-4">Node</h3>
+            <p>The fundamental unit of knowledge. Each node represents a word and has a <code>type</code> (concept, action, property) and a <code>strength</code>, indicating its importance in the network.</p>
+          </div>
         </div>
+        <div class="item">
+           <div class="content" style="padding: 2rem;">
+            <h3 class="title is-4">Spore</h3>
+            <p>A highly efficient persistence mechanism. A "spore" is a serialized (<code>pickle</code>) and compressed snapshot of the Mycelium, allowing the system's learned state to be saved and loaded from a tiny file (~9 MB).</p>
+          </div>
         </div>
       </div>
     </div>
 <section class="section">
   <div class="container is-max-desktop">
+    <!-- Abstract -->
     <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
+        <h2 class="title is-3">Core Philosophy</h2>
         <div class="content has-text-justified">
           <p>
+            The core of MPCA is the "Cognitive Mycelium," a graph-based knowledge structure. Unlike rigid, pre-trained models, the Mycelium is built from the ground up to understand the grammatical and conceptual relationships in language. It learns, forgets, and even "dreams" to form new connections, creating a resilient and emergent form of intelligence with a fraction of the computational overhead of conventional architectures.
           </p>
+          <h2 class="title is-3" style="margin-top: 2rem;">Radical Efficiency: The Spore Advantage</h2>
+           <p>
+            A key breakthrough of the MPCA is its incredible efficiency. A fully-formed Cognitive Mycelium, built from a large dataset (~100k entries, >2.9M sentences), can be compressed into a <code>spore</code> file of only <strong>~9 MB</strong>. This stands in stark contrast to conventional AI models like Transformers (GPT-2 is ~500 MB; modern models are many gigabytes).
           </p>
           <p>
+            This efficiency is a direct result of the architecture's design. Instead of storing billions of statistical weights to predict tokens, the Mycelium stores a compressed graph of concepts and their relationships. It captures knowledge, not just statistical patterns, leading to a powerful, lightweight, and truly novel form of intelligence.
           </p>
         </div>
       </div>
     <!-- Paper video. -->
     <div class="columns is-centered has-text-centered">
       <div class="column is-four-fifths">
+        <h2 class="title is-3">How to Run</h2>
+        <div class="content has-text-justified">
+          <p><strong>1. Install Dependencies:</strong></p>
+          <pre><code>pip install -r requirements.txt
+python -m spacy download en_core_web_sm</code></pre>
+          <p><strong>2. Build a New Mycelium:</strong></p>
+           <p>To create a new knowledge graph from a dataset, run the main script with the <code>--build</code> flag. The default dataset is <code>mlabonne/FineTome-100k</code>.</p>
+          <pre><code>python main.py --build --spore-file mycelium_new.spore --limit 1000</code></pre>
+          <p><strong>3. Interact with an Existing Mycelium:</strong></p>
+           <p>To chat with a pre-built Mycelium, use the <code>--interactive</code> flag.</p>
+          <pre><code>python main.py --interactive --spore-file mycelium.spore</code></pre>
         </div>
       </div>
     </div>
   <div class="container is-max-desktop">
     <div class="columns is-centered">
+      <!-- Lifecycle -->
       <div class="column">
         <div class="content">
+          <h2 class="title is-3">The MPCA Lifecycle</h2>
+           <ol>
+                <li><strong>Genesis (Building):</strong> The <code>Builder</code> creates a Mycelium from a data source, performing grammatical parsing to construct a graph of nodes and relationships, which is then saved as a <code>.spore</code> file.</li>
+                <li><strong>Awakening (Loading):</strong> The system loads a <code>.spore</code> file into memory, awakening the Cognitive Mycelium.</li>
+                <li><strong>Interaction (Solving):</strong> A user asks a question. The <code>Solver</code> takes the core concepts and finds a thought-path through the Mycelium to construct an answer.</li>
+                <li><strong>Evolution (Learning):</strong> Successful thought-paths are reinforced via Hebbian learning, strengthening the system's knowledge.</li>
+                <li><strong>Introspection (Dreaming):</strong> During downtime, the system can dream to form new, speculative connections, expanding its creative potential.</li>
+           </ol>
         </div>
       </div>
+      <!--/ Lifecycle. -->
     </div>
+    <!-- The Path Forward -->
     <div class="columns is-centered">
       <div class="column is-full-width">
+        <h2 class="title is-3">The Path Forward: Extending MPCA to a Multi-Modal World</h2>
         <div class="content has-text-justified">
           <p>
+            The current architecture is a powerful foundation for understanding language. Its true potential lies in extending this conceptual graph to understand and generate other forms of data. The core principle is that MPCA acts as a <strong>central orchestrator</strong>, connecting its abstract conceptual understanding to specialized external models for processing and generation.
           </p>
         </div>
+        <h3 class="title is-4">Image Understanding and Generation</h3>
+        <div class="content has-text-justified">
+            <p><strong>Understanding:</strong> A Vision-Language Model (VLM) like CLIP analyzes an image and outputs concept tags (e.g., "a red car on a street"). The <code>Builder</code> integrates these concepts into the Mycelium, linking an <code>ImageNode</code> to existing nodes like <code>Node('car')</code> and <code>Node('red')</code>. The graph learns <em>what's in the image</em>, not the pixels themselves.</p>
+            <p><strong>Generation:</strong> The <code>Solver</code> assembles a conceptual blueprint (e.g., <code>Node('boat') → Node('blue') → Node('ocean')</code>). This blueprint is passed as a highly-structured prompt to an external image generation model (like a VAE or Diffusion model) to render the final image.</p>
         </div>
+        <h3 class="title is-4">Audio & Video</h3>
         <div class="content has-text-justified">
           <p>
+            A similar approach applies to audio and video. For audio, speech-to-text models provide text for integration, while event detection models can identify non-speech sounds ("dog barking"). For video, an analysis model tracks objects and actions over time, which the <code>Builder</code> represents as a complex, time-stamped sub-graph.
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+        <h3 class="title is-4">Taking Action (Agency)</h3>
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           <p>
+            Actions are a native <code>Node</code> type in MPCA. To enable agency, these action nodes can be linked to real-world API calls or robotic functions. When the <code>Solver</code>'s thought-path traverses an <code>ActionNode</code> linked to an external function (e.g., <code>Node('turn_on_light')</code>), it triggers that function. This turns the MPCA from a passive knowledge base into an active agent that can perceive, reason about, and act upon its environment.
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