diff --git a/build/index.html b/build/index.html index 3ed5e3d..9529fde 100644 --- a/build/index.html +++ b/build/index.html @@ -1,4 +1,4 @@ -Nerfies: Deformable Neural Radiance Fields

Nerfies: Deformable Neural Radiance Fields

Keunhong Park1, Utkarsh Sinha2, Jonathan T. Barron2, Sofien Bouaziz2, Dan B Goldman2, Steven M. Seitz1,2, Ricardo Martin-Brualla2
1University of Washington, 2Google Research
Paper arXiv Video Code Data

Nerfies turns selfie videos from your phone into free-viewpoint portraits.

Abstract

We present the first method capable of photorealistically reconstructing a non-rigidly deforming scene using photos/videos captured casually from mobile phones.

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.

We show that Nerfies 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 "nerfies". 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.

Video

Visual Effects

Using nerfies you can create fun visual effects. This Dolly zoom effect would be impossible without nerfies since it would require going through a wall.

Matting

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.

Animation

Interpolating states

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.

Interpolate start reference image.

Start Frame

Loading...
Interpolation end reference image.

End Frame


Re-rendering the input video

Using Nerfies, you can re-render a video from a novel viewpoint such as a stabilized camera by playing back the training deformations.

Related Links

There's a lot of excellent work that was introduced around the same time as ours.

Progressive Encoding for Neural Optimization introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.

D-NeRF and NR-NeRF both use deformation fields to model non-rigid scenes.

Some works model videos with a NeRF by directly modulating the density, such as Video-NeRF, NSFF, and DyNeRF

There are probably many more by the time you are reading this. Check out Frank Dellart's survey on recent NeRF papers, and Yen-Chen Lin's curated list of NeRF papers.

BibTeX

@article{park2021nerfies,
+LiveCodeBench: FIXME
LiveCodeBench: FIXME
Naman Jain1, King Han1,
1University of California, Berkeley
 Paper  arXiv  Video  Code  Data
Nerfies turns selfie videos from your phone into free-viewpoint portraits.
Abstract
We present the first method capable of photorealistically reconstructing a non-rigidly deforming scene using photos/videos captured casually from mobile phones.
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.
We show that Nerfies 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 "nerfies". 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.
Video
Visual Effects
Using nerfies you can create fun visual effects. This Dolly zoom effect would be impossible without nerfies since it would require going through a wall.
Matting
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.
Animation
Interpolating states
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.
Interpolate start reference image.
Start Frame
Loading...
Interpolation end reference image.
End Frame

Re-rendering the input video
Using Nerfies, you can re-render a video from a novel viewpoint such as a stabilized camera by playing back the training deformations.
Related Links
There's a lot of excellent work that was introduced around the same time as ours.
Progressive Encoding for Neural Optimization introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
D-NeRF and NR-NeRF both use deformation fields to model non-rigid scenes.
Some works model videos with a NeRF by directly modulating the density, such as Video-NeRF, NSFF, and DyNeRF
There are probably many more by the time you are reading this. Check out Frank Dellart's survey on recent NeRF papers, and Yen-Chen Lin's curated list of NeRF papers.
BibTeX
@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},