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 that this drastically
improves quality of hand and face images generated by GANs and Variational
AutoEncoders (VAE).
Improving Geometric Features in Image Generative Models through Augmented Convolution Layers
Image generative models have long struggled with accurately recreating intricate geometric features found in complex objects, such as human hands and fingers. Despite efforts to enhance model size and training datasets, this challenge persists across various models, including denoising diffusion models and Generative Adversarial Networks (GANs), indicating a fundamental limitation in the underlying architecture.
In this paper, we propose a novel approach to address this problem by augmenting convolution layers with enhanced geometric capabilities. Specifically, we introduce a new input channel that incorporates the relative $n$-dimensional Cartesian coordinate system. By providing this additional information during the generation process, we demonstrate how the quality of hand and face images generated by GANs and Variational AutoEncoders (VAEs) can be significantly improved.
The multi-disciplinary nature of this concept is noteworthy. By integrating concepts from geometry and computer vision into image generative models, we bridge the gap between mathematical representations of geometric structures and their effective synthesis in image generation. This approach not only benefits the fields of computer vision and deep learning but also contributes to advancements in areas such as robotics, prosthetics, and virtual reality.
By incorporating the relative $n$-dimensional Cartesian coordinate system as an input channel, the augmented convolution layers gain a deeper understanding of the underlying geometrical features. This allows the model to better capture the intricate details and relationships between different parts of the object being generated.
Our experiments demonstrate the effectiveness of this approach, showcasing significant improvements in the quality and fidelity of generated hand and face images. The enhanced geometric capabilities provided by the augmented convolution layers enable the model to generate images with finer details, improved shapes, and more accurate proportions. This opens up new possibilities for applications such as computer-generated character design, virtual try-on systems, and medical imaging.
In summary, the augmentation of convolution layers with the relative $n$-dimensional Cartesian coordinate system presents a promising solution to address the enduring problem of generating accurate and realistic geometric features in image generative models. Through this multi-disciplinary approach, we pave the way for further advancements in the field of computer vision and its intersection with geometry. Future research may explore extensions of this concept to other domains and investigate the potential of combining additional geometric information for even more precise and lifelike image synthesis.