Work

GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks

Year: 2025

Type: conference-paper

Abstract: The enduring inability of image generative models to recreate intricate geometric features, such as those present in human hands and fingers has been an ongoing problem in image generation for nearly a decade. While strides have been made by increasing model sizes and diversifying training datasets, this issue remains prevalent across all models, from denoising diffusion models to Generative Adversarial Networks (GAN), pointing to a fundamental shortcoming in the underlying architectures. In this paper, we demonstrate how this problem can be mitigated by augmenting convolution layers geometric capabilities through providing them with a single input channel incorporating the relative n-dimensional Cartesian coordinate system. We show this drastically improves quality of images generated by Diffusion Models, GANs, and Variational AutoEncoders (VAE).

Source: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

DOI: 10.1109/wacv61041.2025.00708

Authors: Mehran Hosseini, Peyman Hosseini

Institutions: King's College London, Queen Mary University of London

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: HORIZON EUROPE Framework Programme, Engineering and Physical Sciences Research Council

Award: EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI)