#diffusion-models

#diffusion-models

Diffusion models learn to generate images by understanding and mimicking the underlying probability distribution of image-text pairs.

Diffusion models excel at generic image generation but struggle with specialized requests.

Fine-tuning methods like Dreambooth and ControlNets allow for customization without extensive retraining.

Diffusion models excel in generating high-quality images from detailed textual prompts, surpassing traditional GAN models.

The proposed method surpasses existing models in the quality of 3D reconstruction, particularly in terms of geometry and texture.

The proposed method demonstrates robust generalization capabilities even with fine-tuning on a small-scale 3D object dataset.

The study presents a novel method for 3D object generation using proxy-guided diffusion conditioning.

Wonder3D improves single-view 3D reconstruction quality and consistency using a cross-domain diffusion model that generates multi-view images and textured meshes.

Coin3D enables interactive 3D object generation, utilizing coarse shape proxies and facilitating user-controlled modifications.

The development of an interactive workflow in 3D modeling enables precise part editing and faster reconstruction speeds.

Text-to-image diffusion models enhance image generation by utilizing innovative techniques and architectures.

The inclusion of language models leads to higher quality and better alignment of generated images.

The article introduces a novel method for 3D object generation using proxy-guided diffusion and interactive workflows, advancing the capabilities in computer vision.

The article discusses a novel method for extracting 3D geometries from 2D images using a geometric-aware optimization scheme to handle inaccuracies in generated data.

The model integrates a domain switcher to enhance pre-trained 2D diffusion models for effective operation across multiple domains.

The paper discusses advancements in multi-view generation techniques using diffusion models for 3D reconstruction.

Utilizing 2D diffusion models facilitates improved 3D asset generation and generalization due to limitations in 3D datasets.

The article presents a novel method for generating multi-view consistent images from 3D diffusion models, outperforming existing techniques. Key focus on cross-domain diffusion.

The multi-view cross-domain diffusion scheme enhances 3D generation consistency across views using advanced diffusion techniques.

2D diffusion models enable innovative techniques for generating 3D assets, enhancing efficiency but also presenting challenges with quality and geometric detail.

Diffusion models utilize a forward and reverse Markov chain process for effective image reconstruction from noise.

The study presents a framework for text-driven video editing using text-to-image diffusion models, enhancing visual quality and user control.

This article details the evaluation of 26 text-to-image models across various types, sizes, and accessibility for performance analysis.

General purpose humanoid robotics faces challenges in training for versatile tasks and leveraging generative AI models.

Researchers developed GameNGen, a generative AI game engine simulating Doom dynamically at over 20 FPS using reinforcement and diffusion models.