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Found 32 papers, 14 papers with code
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond.
Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models
We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation.
A Unified Approach for Text- and Image-guided 4D Scene Generation
Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images.
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Then, we train a diffusion model in the 3D-aware latent space, thereby enabling synthesis of high-quality 3D-consistent image samples, outperforming recent state-of-the-art GAN-based methods.
TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models.
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene.
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i. e., videos.
Ranked #5 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.
Score-based Diffusion Models in Function Space
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
Latent Space Diffusion Models of Cryo-EM Structures
The state-of-the-art method cryoDRGN uses a Variational Autoencoder (VAE) framework to learn a continuous distribution of protein structures from single particle cryo-EM imaging data.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
Differentially Private Diffusion Models
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains.
LION: Latent Point Diffusion Models for 3D Shape Generation
To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
Ranked #1 on
Point Cloud Generation
on ShapeNet Airplane
GENIE: Higher-Order Denoising Diffusion Solvers
Synthesis amounts to solving a differential equation (DE) defined by the learnt model.
Ranked #5 on
Image Generation
on AFHQV2
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps
Modern image generative models show remarkable sample quality when trained on a single domain or class of objects.
Generative Adversarial Network
Image-to-Image Translation
+1
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator.
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively.
Ranked #9 on
Image Generation
on CelebA-HQ 256x256
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise.
Ranked #24 on
Image Generation
on CIFAR-10
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
EditGAN: High-Precision Semantic Image Editing
EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing.
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Generative models trained with privacy constraints on private data can sidestep this challenge, providing indirect access to private data instead.
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation.
Ranked #3 on
Indoor Scene Synthesis
on PRO-teXt
2D Semantic Segmentation task 1 (8 classes)
3D Semantic Scene Completion
+1
Causal Scene BERT: Improving object detection by searching for challenging groups
We verify that the prioritized groups found via intervention are challenging for the object detector and show that retraining with data collected from these groups helps inordinately compared to adding more IID data.
Score-based Generative Modeling in Latent Space
Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling.
Ranked #3 on
Image Generation
on CIFAR-10
(FD metric)
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts.
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality.
Differentially Private Generative Models Through Optimal Transport
Generative models trained with privacy constraints on private data can sidestep this challenge and provide indirect access to the private data instead.
Variational Amodal Object Completion
In images of complex scenes, objects are often occluding each other which makes perception tasks such as object detection and tracking, or robotic control tasks such as planning, challenging.
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples.
Ranked #1 on
Image Generation
on Stacked MNIST
