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<html>
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<meta charset="utf-8">
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<meta name="description"
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content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
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<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
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<span class="author-block">
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<a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
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<span>Paper</span>
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<a href="https://arxiv.org/abs/2011.12948"
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<span>Data</span>
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<h2 class="subtitle has-text-centered">
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<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
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free-viewpoint
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portraits.
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<h2 class="title is-3">Abstract</h2>
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<p>
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Our approach augments neural radiance fields
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(NeRF) by optimizing an
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additional continuous volumetric deformation field that warps each observed point into a
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canonical 5D NeRF.
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We observe that these NeRF-like deformation fields are prone to local minima, and
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propose a coarse-to-fine optimization method for coordinate-based models that allows for
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more robust optimization.
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By adapting principles from geometry processing and physical simulation to NeRF-like
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models, we propose an elastic regularization of the deformation field that further
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improves robustness.
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</p>
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<p>
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We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
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photos/videos into deformable NeRF
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models that allow for photorealistic renderings of the subject from arbitrary
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viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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using a
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rig with two mobile phones that take time-synchronized photos, yielding train/validation
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images of the same pose at different viewpoints. We show that our method faithfully
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reconstructs non-rigidly deforming scenes and reproduces unseen views with high
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fidelity.
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<h2 class="title is-3">Visual Effects</h2>
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<p>
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Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
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would be impossible without nerfies since it would require going through a wall.
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</p>
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<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
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<h2 class="title is-3">Matting</h2>
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<p>
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As a byproduct of our method, we can also solve the matting problem by ignoring
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samples that fall outside of a bounding box during rendering.
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</p>
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<h3 class="title is-4">Interpolating states</h3>
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<p>
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We can also animate the scene by interpolating the deformation latent codes of two input
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frames. Use the slider here to linearly interpolate between the left frame and the right
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frame.
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class="interpolation-image"
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alt="Interpolate start reference image."/>
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<p>Start Frame</p>
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id="interpolation-slider"
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step="1" min="0" max="100" value="0" type="range">
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class="interpolation-image"
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alt="Interpolation end reference image."/>
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<p class="is-bold">End Frame</p>
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<h3 class="title is-4">Re-rendering the input video</h3>
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Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
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viewpoint such as a stabilized camera by playing back the training deformations.
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There's a lot of excellent work that was introduced around the same time as ours.
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</p>
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<p>
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<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.
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</p>
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<p>
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<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>
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both use deformation fields to model non-rigid scenes.
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</p>
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<p>
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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>
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</p>
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<p>
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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>.
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<!--/ Concurrent Work. -->
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<section class="section" id="BibTeX">
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<div class="container is-max-desktop content">
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{park2021nerfies,
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year = {2021},
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}</code></pre>
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<h1 class="title is-1 publication-title">Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesis</h1>
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<a href="https://sailor-z.github.io/">Chen Zhao</a><sup>1</sup>,</span>
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<a href="https://xuanwangvc.github.io/">Xuan Wang</a><sup>2</sup>,</span>
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<a href="https://sites.google.com/view/tong-zhang">Tong Zhang</a><sup>1</sup>,</span>
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<a href="https://saqibjaved1.github.io/">Saqib Javed</a><sup>1</sup>,</span>
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<a href="https://people.epfl.ch/mathieu.salzmann?lang=en">Mathieu Salzmann</a><sup>13</sup>
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<span class="author-block"><sup>1</sup>EPFL-CVLab,</span>
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<span class="author-block"><sup>2</sup>Ant Group,</span>
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<span class="author-block"><sup>3</sup>Swiss Data Science Center</span>
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<h2 class="title is-3">Novel View Synthesis</h2>
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3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness for novel view synthesis (NVS). However, the 3DGS model tends to overfit when trained with sparse posed views, limiting its generalization ability to novel views. We alleviate the overfitting problem, presenting a Self-Ensembling Gaussian Splatting (SE-GS) approach. Our method encompasses a Σ-model and a Δ-model. The Σ-model serves as an ensemble of 3DGS models that generates novel-view images during inference. We achieve the self-ensembling by introducing an uncertainty-aware perturbation strategy at the training state. We complement the Σ-model with the Δ-model, which is dynamically perturbed based on the uncertainties of novel-view renderings across different training steps. The perturbation yields diverse 3DGS models without additional training costs.
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<h2 class="title is-3">Method Overview</h2>
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The perturbed models are derived from the Δ-model via an uncertainty-aware perturbation strategy. We store images rendered from pseudo views at different training steps in buffers, from which we compute pixel-level uncertainties. We then perturb the Gaussians overlapping the pixels with high uncertainties, as highlighted as red ellipses. Self-ensembling over the perturbed models is achieved by training a Σ-model with a regularization that penalizes the discrepancies of the Σ-model and the perturbed Δ-model. During inference, novel view synthesis is performed using the Σ-model.
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<h2 class="title is-3">Pseudo-View Renderings</h2>
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<pre><code>@article{park2021nerfies,
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year = {2021},
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}</code></pre>
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