We introduce TurboEdit -- simple text-based image editing in... 1/2 sec! We leverage few-step diffusion models, mapping real images into noise with an encoder. Please see our #ECCV2024 paper: Work w/ Zongze Wu, Nick Kolkin, Jon Brandt, Eli Shechtman 1/show more

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper... page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.show more

(1/3) Can we turn text-to-image models into photorealistic 3D... generators? ViewDiff (#CVPR2024) produces realistic, multi-view consistent images of real-world 3D objects in authentic surroundings. Website Video How does it work?show more

Create a 3D model from a single image, set... of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”show more

Can we use motion to prompt diffusion models? Our... #ICLR2024 paper does just that. We propose Motion Guidance, a technique that allows users to edit an image by specifying “where things should move.”show more

Diffusion models have amazing image creation abilities. But how... well does their generative knowledge transfer to discriminative tasks? We present Diffusion Classifier: strong classification results with pretrained conditional diffusion models, *with no additional training*! 1/9show more

Diffusion models are sensitive to small changes in the... input noise. We introduce Alias-Free Latent Diffusion Models (𝗔𝗙-𝗟𝗗𝗠) at #CVPR2025. It achieves shift-equivariance and generates consistent outputs. Project: arXiv:show more

Snap presents MoA Mixture-of-Attention for Subject-Context Disentanglement in Personalized... Image Generation We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Expertsshow more

This is the coolest Diffusion work I've seen in... a while! It generates Visual Anagrams, a type of optical illusion where an image looks like one thing, but changes appearance when transformed. It works with any orthogonal transformation matrices, which luckily include rotation, permutation (jigsaw puzzles), and color negation. Intuitively, the method first inverts the noise from multiple image transforms (with different text prompts), and then average them. After taking a diffusion step in the averaged noise, the resulting image becomes an anagram that aligns with the texts in different views. It does very little computation, using pre-trained Stable Diffusion. Simple, elegant, and inexpensive technique for non-professionals to create some interesting art! Paper: Website: It's open-source! Authors: Daniel Geng, Inbum Park, Andrew Owens.show more

🚀 Introducing GenLit – Reformulating Single-Image Relighting as Video... Generation! We leverage video diffusion models to perform realistic near-field relighting from just a single image—No explicit 3D reconstruction or ray tracing required! No intermediate graphics buffers, directly in the pixel space! 📄 Dive into the paper: 🎥 Project page & demos: 🛠 Code coming soon! #GenerativeAI #ComputerVision #Relighting #DiffusionModels #Graphics 🧵 1/5show more

Accepted at #ECCV2024 🇮🇹 See you in Milan! PolyOculus:... Simultaneous Multi-view Image-based Novel View Synthesis Jason Yu Tristan Aumentado-Armstrong Fereshteh Forghani Marcus Brubaker and me Paper:show more

NVIDIA just released a very impressive text-to-video paper. Video... Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:show more

Diffusion models are great, but we can squeeze out... so much more from them. The only problem is that it usually requires extra training or manual representation editing. In our new paper, we show that with the current capabilities of LLMs, it is much simpler than we thought!show more

1/ Happy to share UniDisc - Unified Multimodal Discrete... Diffusion – We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.show more

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field Contributions:... 1. Based on our analysis of weather editing characteristics, we introduce WeatherEdit, a comprehensive and efficient framework for realistic and controllable weather generation. Compared with existing methods that focus on either background editing or static weather effects, a progressive 2D-to-4D transformation process in WeatherEdit enhances adaptability across a wider range of scenarios. 2. We introduce an all-in-one adapter to enable a diffusion model for multi-weather (snowy, rainy, and fog) synthesis, along with a Temporal-View attention to ensure consistent editing across multi-frame and multi-view. 3. We design a 4D Gaussian field for weather particle modeling, enabling plausible simulation of raindrops, snowflakes, and fog with controllable severity. 4. We demonstrate WeatherEdit’s effectiveness in generating realistic, consistent, and controllable weather effects in 3D driving scenes, showcasing its applicability to real-world scenarios.show more

(1/2) Intrinsic Image Diffusion for Single-view Material Estimation! We... propose a probabilistic diffusion model to handle material &amp; lighting ambiguities. We obtain sharp material estimates and facilitate high-fidelity relighting.show more

Step 1: Generate the base image with GPT-2 Step... 2: Serve the image you just generated back to GPT-2 with this prompt: "Convert this scene into a 360 equirectangular image"show more

Big Marigold update! Last year, we showed how to... turn Stable Diffusion 2 into a SOTA depth estimator with a few synthetic samples and 2–3 days on just 1 GPU. Today's release features: 🏎️ 1-step inference 🔢 New modalities 🫣 High resolution 🧨 Diffusers support 🕹️ New demos 🧶👇show more

#ECCV24 We introduce Controllable Human-Object Interaction Synthesis (CHOIS) to... generate interactions given text in a 3D scene. w alex, Roozbeh Mottaghi Jiajun Wu Xavier Puig Karen. code: project page:show more

The Hidden Language of Diffusion Models paper page: tackle... the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulationshow more

(1/2) Check out "𝐏𝐨𝐥𝐲𝐃𝐢𝐟𝐟: Generating 3D Polygonal Meshes with... Diffusion Models"! Our model operates directly on the polygons of 3D meshes and generates novel shapes as output through an iterative diffusion process.show more