#diffusion-models

#diffusion-models

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 generate high-quality data by gradually adding noise and then learning to reverse the process.

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.

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

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

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.