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Found 21 papers, 11 papers with code
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency.
Customizing Text-to-Image Diffusion with Camera Viewpoint Control
Model customization introduces new concepts to existing text-to-image models, enabling the generation of the new concept in novel contexts.
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.
One-Step Image Translation with Text-to-Image Models
In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired data for model fine-tuning.
Multi-Scale Semantic Segmentation with Modified MBConv Blocks
Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks.
Jump Cut Smoothing for Talking Heads
We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks.
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.
Holistic Evaluation of Text-To-Image Models
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption.
Expressive Text-to-Image Generation with Rich Text
For each region, we enforce its text attributes by creating region-specific detailed prompts and applying region-specific guidance, and maintain its fidelity against plain-text generation through region-based injections.
Scaling up GANs for Text-to-Image Synthesis
From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models.
Ranked #18 on
Image Generation
on ImageNet 256x256
Domain Expansion of Image Generators
First, we note the generator contains a meaningful, pretrained latent space.
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
BlobGAN: Spatially Disentangled Scene Representations
Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network.
Contrastive Feature Loss for Image Prediction
Training supervised image synthesis models requires a critic to compare two images: the ground truth to the result.
Mitigating Mode Collapse by Sidestepping Catastrophic Forgetting
Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator.
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation.
Contrastive Learning for Unpaired Image-to-Image Translation
Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset.
Swapping Autoencoder for Deep Image Manipulation
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging.
Semantic Image Synthesis with Spatially-Adaptive Normalization
Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers.
Ranked #3 on
Sketch-to-Image Translation
on COCO-Stuff
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Domain adaptation is critical for success in new, unseen environments.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
Ranked #1 on
Image-to-Image Translation
on zebra2horse
(Frechet Inception Distance metric)
Multimodal Unsupervised Image-To-Image Translation
Style Transfer
+2
