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<li class="toctree-l2"><a class="reference internal" href="../../reference/cli/ns_install_cli.html">ns-install-cli</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../reference/cli/ns_train.html">ns-train</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="signerf">
<h1>SIGNeRF<a class="headerlink" href="#signerf" title="Permalink to this heading">#</a></h1>
<h3> Scene Integrated Generation for Neural Radiance Fields</h3>
<p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-outline-primary reference external" href="https://signerf.jdihlmann.com/"><span>Website &amp; Code</span></a></p>
<video id="teaser" muted autoplay playsinline loop controls width="100%">
<source id="mp4" src="https://signerf.jdihlmann.com/videos/SIGNeRF_small.mp4" type="video/mp4">
</video>
<p><strong>Generatively edits NeRF scenes in a controlled and fast manner.</strong></p>
<p>SIGNeRF allows for generative 3D scene editing. We present a novel approach to combine <a class="reference external" href="https://www.matthewtancik.com/nerf">NeRFs</a> as scene representation with the image diffusion model <a class="reference external" href="https://github.com/Stability-AI/stablediffusion">StableDiffusion</a> to allow fast and controlled 3D generation.</p>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2>
<p>Install nerfstudio dependencies. Then run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/cgtuebingen/SIGNeRF
</pre></div>
</div>
<p>SIGNeRF requires to use <a class="reference external" href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Stable Diffusion Web UI</a>. For detailed installation information follow our <a class="reference external" href="https://github.com/cgtuebingen/SIGNeRF?tab=readme-ov-file#installation">installation guide</a>.</p>
</section>
<section id="running-signerf">
<h2>Running SIGNeRF<a class="headerlink" href="#running-signerf" title="Permalink to this heading">#</a></h2>
<p>Details for running SIGNeRF can be found <a class="reference external" href="https://github.com/cgtuebingen/SIGNeRF">here</a>. Once installed, run:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ns-train<span class="w"> </span>signerf<span class="w"> </span>--help
</pre></div>
</div>
<p>Two variants of SIGNeRF are provided:</p>
<div class="table-wrapper colwidths-auto docutils container">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Method</p></th>
<th class="head"><p>Description</p></th>
<th class="head"><p>Time</p></th>
<th class="head"><p>Quality</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">signerf</span></code></p></td>
<td><p>Full model, used in paper</p></td>
<td><p>40min</p></td>
<td><p>Best</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">signerf_nerfacto</span></code></p></td>
<td><p>Faster model with Nerfacto</p></td>
<td><p>20min</p></td>
<td><p>Good</p></td>
</tr>
</tbody>
</table>
</div>
<p>For more information on hardware requirements and training times, please refer to the <a class="reference external" href="https://github.com/cgtuebingen/SIGNeRF?tab=readme-ov-file#training-1">training section</a>.</p>
</section>
<section id="interface">
<h2>Interface<a class="headerlink" href="#interface" title="Permalink to this heading">#</a></h2>
<a class="reference internal image-reference" href="https://github.com/cgtuebingen/SIGNeRF/raw/main/images/interface.png"><img alt="SIGNeRF Interface" src="https://github.com/cgtuebingen/SIGNeRF/raw/main/images/interface.png" style="width: 100%;" /></a>
<p>SIGNeRF fully integrates into the Nerfstudio <a class="reference external" href="https://viser.studio">viser</a> interface. It allows for easy editing of NeRF scenes with a few simple clicks. The user can select the editing method, the region to edit, and the object to insert. The reference sheet can be previewed and the NeRF is fine-tuned on the edited images. If you are interested in how we fully edit the Nerfstudio interface please find our code <a class="reference external" href="https://github.com/cgtuebingen/SIGNeRF">here</a>.</p>
</section>
<section id="method">
<h2>Method<a class="headerlink" href="#method" title="Permalink to this heading">#</a></h2>
<section id="overview">
<h3>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">#</a></h3>
<p>SIGNeRF is a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. We introduce a new generative update strategy that ensures 3D consistency across the edited images, without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights, we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model, we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.</p>
<p>For an in-depth visual explanation and our results please watch our <a class="reference external" href="https://www.youtube.com/playlist?list=PL5y23CB9WmildtW3QyMEi3arXg06zB4ex">videos</a> or read our <a class="reference external" href="https://arxiv.org/abs/2401.01647">paper</a>.</p>
</section>
</section>
<section id="pipeline">
<h2>Pipeline<a class="headerlink" href="#pipeline" title="Permalink to this heading">#</a></h2>
<video id="pipeline" muted autoplay playsinline loop controls width="100%">
<source id="mp4" src="https://signerf.jdihlmann.com/videos/pipeline_compressed.mp4" type="video/mp4">
</video>
<p>We leverage the strengths of <a class="reference external" href="https://github.com/lllyasviel/ControlNet">ControlNet</a>, a depth condition image diffusion model, to edit an existing NeRF scene. We do so with a few simple steps in a single forward pass:</p>
<ol class="arabic simple" start="0">
<li><p>We start with an original NeRF scene and select an editing method / region</p></li>
<li><p>For object generation, we place a mesh object into the scene</p></li>
<li><p>And control the precise location and shape of the edit</p></li>
<li><p>We position reference cameras in the scene</p></li>
<li><p>Render the corresponding color, depth, and mask images, and arrange them into image grids</p></li>
<li><p>These grids are used to generate the reference sheet with conditioned image diffusion</p></li>
<li><p>Generate new edited images consistent with the reference sheet by leveraging an inpainting mask.</p></li>
</ol>
<ul class="simple">
<li><p>Repeat step (6) for all cameras</p></li>
</ul>
<ol class="arabic simple" start="7">
<li><p>Finally, the NeRF is fine-tuned on the edited images</p></li>
</ol>
<section id="reference-sheet-generation">
<h3>Reference Sheet Generation<a class="headerlink" href="#reference-sheet-generation" title="Permalink to this heading">#</a></h3>
<a class="reference internal image-reference" href="https://arxiv.org/html/2401.01647v2/x4.png"><img alt="Reference Sheet Generation" src="https://arxiv.org/html/2401.01647v2/x4.png" style="width: 100%;" /></a>
<p>SIGNeRF uses a novel technique called reference sheet generation. We observe that the image
diffusion model <a class="reference external" href="https://github.com/lllyasviel/ControlNet">ControlNet</a> can already generate multiview consistent images of a scene without the need for iterative refinement like <a class="reference external" href="https://instruct-nerf2nerf.github.io/">Instruct-NeRF2NeRF</a>. While generating individual views sequentially introduces too much variation to integrate them into a consistent 3D model, arranging them in a grid of images that are processed by ControlNet in one pass significantly
improves the multi-view consistency. Based on the depth maps rendered from the original NeRF
scene we employ a depth-conditioned inpainting variant of ControlNet to generate such a reference sheet of the edited scene. A mask specifies the scene region where the generation should occur. This step gives a lot of control to the user. Different appearances can be produced by generating reference sheets with different seeds or prompts. The one sheet finally selected will directly determine the look of the final 3D scene.</p>
<p>If you want to learn more about the method, please read our <a class="reference external" href="https://arxiv.org/abs/2401.01647">paper</a> or read the breakdown of the method in the <a class="reference external" href="https://radiancefields.com/controlnet-nerfs-signerf/">Radiance Fields article</a>.</p>
</section>
</section>
<section id="related-work">
<h2>Related Work<a class="headerlink" href="#related-work" title="Permalink to this heading">#</a></h2>
<p>SIGNeRF was inspired by the work of <a class="reference external" href="https://instruct-nerf2nerf.github.io/">Instruct-NeRF2NeRF</a> which is also available in the <a class="reference external" href="https://docs.nerf.studio/nerfology/methods/in2n.html">Nerfology</a> documentation.</p>
</section>
<section id="authors">
<h2>Authors<a class="headerlink" href="#authors" title="Permalink to this heading">#</a></h2>
<p>SIGNeRF was developed by <a class="reference external" href="https://jdihlmann.com">Jan-Niklas Dihlmann</a> and <a class="reference external" href="https://aengelhardt.com/">Andreas Engelhardt</a>.</p>
</section>
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<ul>
<li><a class="reference internal" href="#">SIGNeRF</a><ul>
<li><a class="reference internal" href="#installation">Installation</a></li>
<li><a class="reference internal" href="#running-signerf">Running SIGNeRF</a></li>
<li><a class="reference internal" href="#interface">Interface</a></li>
<li><a class="reference internal" href="#method">Method</a><ul>
<li><a class="reference internal" href="#overview">Overview</a></li>
</ul>
</li>
<li><a class="reference internal" href="#pipeline">Pipeline</a><ul>
<li><a class="reference internal" href="#reference-sheet-generation">Reference Sheet Generation</a></li>
</ul>
</li>
<li><a class="reference internal" href="#related-work">Related Work</a></li>
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