16 citations · 48 across the 11 of their papers we have counts for
11 papers · 1 filter
T-DIG: Text-Conditioned Diversity for Image Generation
Simran Khanuja, Rohit Gandikota, Gaurav Parmar +1
Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent. However, T2…
WarpI2I: Image Warping for Image-to-Image Translation
Shen Zheng, Anurag Ghosh, Gaurav Parmar +1
Image-to-image (I2I) translation has achieved strong results in tasks like human relighting and driving scene translation using latent diffusion models (LDMs). However, compact LDM…
Scaling Group Inference for Diverse and High-Quality Generation
Gaurav Parmar, Or Patashnik, Daniil Ostashev +4
Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, i…
Object-level Visual Prompts for Compositional Image Generation
Gaurav Parmar, Or Patashnik, Kuan-Chieh Wang +5
We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent composit…
On the Content Bias in Fréchet Video Distance
Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar +2
Fréchet Video Distance (FVD), a prominent metric for evaluating video generation models, is known to conflict with human perception occasionally. In this paper, we aim to explore t…
One-Step Image Translation with Text-to-Image Models
Gaurav Parmar, Taesung Park, Srinivasa Narasimhan +1
In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired da…
Zero-shot Image-to-Image Translation
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang +3
Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply th…
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
Gaurav Parmar, Yijun Li, Jingwan Lu +3
Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categori…
On Aliased Resizing and Surprising Subtleties in GAN Evaluation
Gaurav Parmar, Richard Zhang, Jun-Yan Zhu
Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The often-used Frechet Inception Distance (FID) metric, for example, extr…
Dual Contradistinctive Generative Autoencoder
Gaurav Parmar, Dacheng Li, Kwonjoon Lee +1
We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthes…
Guided Variational Autoencoder for Disentanglement Learning
Zheng Ding, Yifan Xu, Weijian Xu +4
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement lear…