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Found 19 papers, 9 papers with code
Improved Distribution Matching Distillation for Fast Image Synthesis
Recent approaches have shown promises distilling diffusion models into efficient one-step generators.
Editable Image Elements for Controllable Synthesis
In this work, we propose an image representation that promotes spatial editing of input images using a diffusion model.
Lazy Diffusion Transformer for Interactive Image Editing
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
Learning Subject-Aware Cropping by Outpainting Professional Photos
How to frame (or crop) a photo often depends on the image subject and its context; e. g., a human portrait.
One-step Diffusion with Distribution Matching Distillation
We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality.
Materialistic: Selecting Similar Materials in Images
We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area.
Semi-supervised Parametric Real-world Image Harmonization
Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo.
Domain Expansion of Image Generators
First, we note the generator contains a meaningful, pretrained latent space.
Self-Supervised Burst Super-Resolution
Extensive experiments on real and synthetic data show that, despite only using noisy bursts during training, models trained with our self-supervised strategy match, and sometimes surpass, the quality of fully-supervised baselines trained with synthetic data or weakly-paired ground-truth.
Spotting Temporally Precise, Fine-Grained Events in Video
We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur).
Ranked #6 on
Action Spotting
on SoccerNet-v2
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
This leads to poor accuracy when downstream tasks, such as action recognition, depend on pose.
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation.
Modulated Periodic Activations for Generalizable Local Functional Representations
Our approach produces generalizable functional representations of images, videos and shapes, and achieves higher reconstruction quality than prior works that are optimized for a single signal.
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.
Differentiable Vector Graphics Rasterization for Editing and Learning
We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content.
Basis Prediction Networks for Effective Burst Denoising with Large Kernels
Bursts of images exhibit significant self-similarity across both time and space.
Deep Bilateral Learning for Real-Time Image Enhancement
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
Convolutional Neural Network for Earthquake Detection and Location
The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment.
Geophysics
