Gradient Dude

2022-07-11 16:00:07 Partial conv vs Gated conv. Figures for the post above. Open / Comment

2022-07-11 16:00:07 ​​Image Inpainting: Partial Convolution vs Gated Convolution

Let’s talk about some essential components of the image inpainting networks - convolutions. #fundamentals

It is common in image inpainting model to feed a corrupted image (with some parts masked out) to the generator network. But we don’t want the network layers to rely on empty regions when features are computed. There is a straightforward solutions to this problem (Partial Convolutions) and a more elegant one (gated convolution).

Partial Convolutions make the convolutions dependent only on valid pixels. They are like normal convolutions, but with hard mask multiplication applied to each output feature map. The first map is computed from occluded image directly or provided as an input from user. Masks for every next partial convolution are computed by finding non-zero elements in the input feature maps.

- Partial convolution heuristically classifies all spatial locations to be either valid or invalid. The mask in next layer will be set to ones no matter how many pixels are covered by the filter range in previous layer (for example, for a 3x3 conv, 1 valid pixel and 9 valid pixels are treated as same to update current mask).
- For partial convolution the invalid pixels will progressively disappear in deep layers, gradually converting all mask values to ones.
- partial convolution is incompatible with additional user inputs. However, we would like to be able to utilize extra user inputs for conditional generation (for example, sparse sketch inside the mask).
- All channels in each layer share the same mask, which limits the flexibility. Essentially, partial convolution can be viewed as un-learnable single-channel feature hard-gating.

Gated convolutions. Instead of hard-gating mask updated with rules, gated convolutions learn soft mask automatically from data. It has a “Soft gating” block (consists of one convolutional layer) which takes an input feature map and predicts an appropriate soft mask which is applied to the output of the convolution.

- Can take any extra user guidance (e.g., mask, sketch) as input. They can be all concatenated with the corrupted image and fed to the first gated convolution.
- Learns a dynamic feature selection mechanism for each channel and each spatial
location.
- Interestingly, visualization of intermediate gating values show that it learns to select the feature not only according to background, mask, sketch, but also considering semantic segmentation in some channels.
- Even in deep layers, gated convolution learns to highlight the masked regions and sketch information in separate channels to better generate inpainting results.

@gradientdude Open / Comment

2022-05-26 15:28:26 Neural 3D Reconstruction in the Wild”
[SIGGRAPH 2022]

When will neural-based approaches beat COLMAP in terms of both speed and quality of the surface reconstruction? Here is the Neural Rendering method tackling the quality part.

Authors show that with a clever sampling strategy, neural-based 3D reconstruction for large scenes in the wild can be better and faster than COLMAP.

The method is built on top of NeuS (2 MLPs for prediction color and SDF for (x,y,z) loсation). The main contribution is a hybrid voxel- and surface guided sampling technique that allows for more efficient ray sampling around surfaces and leads to significant improvements in
reconstruction quality (see figure attached).

❱❱ Project page
Code is coming soon

@Artem Gradient Open / Comment

2022-05-01 19:32:27 Chinese researchers brought deepfakes to the next level by changing the entire head

We have all seen deepfakes where faces are swapped. This paper went further, they substitute the driving head entirely. Miracles of Chinese engineering skills and a lot of losses do the job .

Compared to the usual "face swap", the new method exhibits better transfer of the personality from the target photo to the driving video, preserving the hair, eyebrows, and other important attributes. A slight improvement of the temporal stability is needed though - the edges of the head are a little twitchy. There is no code yet, but the authors promised to upload it soon.

❱❱ Few-Shot Head Swapping in the Wild CVPR 2022, Oral Open / Comment

2022-05-01 19:31:10 I'm back people! After some pause I decided to continue posting in this channel. I promise to select the most interesting papers and write at least 1-2 posts per week.

Cheers,
Artsiom Open / Comment

2021-11-13 02:11:32 ​​On Neural Rendering

What is Neural Rendering? In a nutshell, neural rendering is when we take classic algorithms for image rendering from computer graphics and replace a part of the pipeline with neural networks (stupid, but effective). Neural rendering learns to render and represent a scene from one or more input photos by simulating the physical process of a camera that captures the scene. A key property of 3D neural rendering is the disentanglement of the camera capturing process (i.e., the projection and image formation) and the representation of a 3D scene during training. That is, we learn an explicit (voxels, point clouds, parametric surfaces) or an implicit (signed distance function) representation of a 3D scene. For training, we use observations of the scene from several camera viewpoints. The network is trained on these observations by rendering the estimated 3D scene from the training viewpoints, and minimizing the difference between the rendered and observed images. This learned scene representation can be rendered from any virtual camera in order to synthesize novel views. It is important for learning that the entire rendering pipeline is differentiable.

You may have noticed, that the topic of neural rendering, including all sorts of nerfs-schmerfs, is now a big hype in computer vision. You might say that neural rendering is very slow, and you'd be right. A typical training session on a small scene with ~ 50 input photos takes about 5.5 hours for the fastest method on a single GPU, but neural rendering methods have made significant progress in the last year improving both fidelity and efficiency. To catch up on all the recent developments in this direction, I highly recommend reading this SOTA report "Advances in Neural Rendering".

The gif is from Volume Rendering of Neural Implicit Surfaces paper. Open / Comment

2021-10-12 02:20:52 StyleGAN3 by NVIDIA!

Do you remember the awesome smooth results by Alias-Free GAN I wrote about earlier? The authors have finally posted the code and now you can build your amazing projects on it.

I don't know about you, but my hands are already itching to try it out.

Source code
Project page
Colab Open / Comment

2021-08-11 10:00:22 Here's another example of how the algorithm mentioned above works. The horse is stylized in Kirchner's style.

The differences with the classic pixel-by-pixel method by Gatys et al. are very explicit. The new method, of course, significantly perturbs the content in contrast to Gatys, but the style really looks more similar to the German expressionist Kirchner and we see prominent brush strokes. Open / Comment

2021-08-11 10:00:22 Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes

[Another recent "style transfer with brushstrokes" paper from my colleagues in Heidelberg University ]

In this paper, images are stylized by optimizing parameterized brush strokes instead of pixels as well. In order to backpropagate teh error through the rendered brushstrokes, the authors came up with a simple differentiable rendering of strokes, each of which is parameterized with a Bezier curve.

The results are excellent. You can also add constraints to the shape of your brush strokes by drawing a couple of lines over the photo. The only drawback is that it works for a rather long time (10-20 minutes per 1MP picture), since this is an iterative optimization, and at each iteration a forward-pass through VGG-16 newtwork is required.

Project website
Source code Open / Comment

2021-08-10 15:42:02 One more example of a synthesised image by the paper mentioned above. Open / Comment