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Found 30 papers, 14 papers with code
PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis
This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views.
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster.
Dual-Camera Joint Deblurring-Denoising
Recent image enhancement methods have shown the advantages of using a pair of long and short-exposure images for low-light photography.
Watch Your Steps: Local Image and Scene Editing by Text Instructions
A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made.
Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models
In this paper, we propose a novel generative model capable of producing a sequence of photorealistic images consistent with a specified camera trajectory, and a single starting image.
Reference-guided Controllable Inpainting of Neural Radiance Fields
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools.
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields
We refer to this task as 3D inpainting.
Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata
Our experiments show that our learned sampling can adapt to the image content to produce better raw reconstructions than existing methods.
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images
This paper proposes a framework for training a noise model and a denoiser simultaneously while relying only on pairs of noisy images rather than noisy/clean paired image data.
Modeling sRGB Camera Noise with Normalizing Flows
The nonlinear steps on the ISP culminate in a significantly more complex noise distribution in the sRGB domain and existing raw-domain noise models are unable to capture the sRGB noise distribution.
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Coordinate networks like Multiplicative Filter Networks (MFNs) and BACON offer some control over the frequency spectrum used to represent continuous signals such as images or 3D volumes.
Auto White-Balance Correction for Mixed-Illuminant Scenes
Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination.
Neural Image Representations for Multi-Image Fusion and Layer Separation
We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations.
Zero-shot Learning with Class Description Regularization
The purpose of generative Zero-shot learning (ZSL) is to learning from seen classes, transfer the learned knowledge, and create samples of unseen classes from the description of these unseen categories.
Continuous Latent Process Flows
Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines.
CAMS: Color-Aware Multi-Style Transfer
A nice feature of our method is that it enables the users to manually select the color associations between the target style and content image for more transfer flexibility.
Manifold Density Estimation via Generalized Dequantization
For instance, some kinds of data may be known to lie on the surface of a sphere.
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs.
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distributions like images.
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.
Variational Hyper RNN for Sequence Modeling
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence.
Normalizing Flows: An Introduction and Review of Current Methods
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact.
Noise Flow: Noise Modeling with Conditional Normalizing Flows
Modeling and synthesizing image noise is an important aspect in many computer vision applications.
Ranked #8 on
Image Denoising
on SID x300
Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture
To our knowledge, this is the first approach to take physics into account without explicit {\em a priori} knowledge of the environment or body dimensions.
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics.
Find your Way by Observing the Sun and Other Semantic Cues
In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world.
Building Proteins in a Day: Efficient 3D Molecular Reconstruction
A new framework for estimation is introduced which relies on modern stochastic optimization techniques to scale to large datasets.
Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM
Determining the 3D structures of biological molecules is a key problem for both biology and medicine.
Efficient Optimization for Sparse Gaussian Process Regression
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression.
Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization
In this paper we propose an affordable solution to selflocalization, which utilizes visual odometry and road maps as the only inputs.
