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Found 5 papers, 2 papers with code
MatFuse: Controllable Material Generation with Diffusion Models
Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise.
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
We evaluate the effectiveness of this approach by training a semantic segmentation model on a real dataset augmented in two ways: 1) using synthetic images obtained from real masks, and 2) generating images from synthetic semantic masks.
Transformer-based Image Generation from Scene Graphs
In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability.
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
In recent years, deep learning has been successfully applied in various scientific domains.
Transforming Image Generation from Scene Graphs
Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to alternatives such as class labels or text descriptions.
