127 citations · 155 across the 8 of their papers we have counts for
10 papers
Providing Rapid Design Feedback for 3D Obstacle Course Games Using Constrained Solvability Queries
Zander Majercik, Sharon Zhang, William Wang +6
We present a system that aids the design of 3D obstacle course games by providing designers with rapid feedback on how obstacles can be solved. Our core contribution is a system fo…
PreciseControl: Enhancing Text-To-Image Diffusion Models with Fine-Grained Attribute Control
Rishubh Parihar, Sachidanand VS, Sabariswaran Mani +2
Recently, we have seen a surge of personalization methods for text-to-image (T2I) diffusion models to learn a concept using a few images. Existing approaches, when used for face pe…
Exploring Attribute Variations in Style-based GANs using Diffusion Models
Rishubh Parihar, Prasanna Balaji, Raghav Magazine +4
Existing attribute editing methods treat semantic attributes as binary, resulting in a single edit per attribute. However, attributes such as eyeglasses, smiles, or hairstyles exhi…
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
Harsh Rangwani, Lavish Bansal, Kartik Sharma +3
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulat…
Improving GANs for Long-Tailed Data through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali +2
Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few sa…
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
Tejan Karmali, Rishubh Parihar, Susmit Agrawal +4
Progress in GANs has enabled the generation of high-resolution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images vi…
Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration
Rishubh Parihar, Ankit Dhiman, Tejan Karmali +1
Unconstrained Image generation with high realism is now possible using recent Generative Adversarial Networks (GANs). However, it is quite challenging to generate images with a giv…
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha +3
In this work, we introduce LEAD, an approach to discover landmarks from an unannotated collection of category-specific images. Existing works in self-supervised landmark detection…
Deep Implicit Surface Point Prediction Networks
Rahul Venkatesh, Tejan Karmali, Sarthak Sharma +4
Deep neural representations of 3D shapes as implicit functions have been shown to produce high fidelity models surpassing the resolution-memory trade-off faced by the explicit repr…
Fashionable Modelling with Flux
Michael Innes, Elliot Saba, Keno Fischer +6
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in…