1 code implementation • 18 Apr 2024 • Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu, Jia-Bin Huang
We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality.
1 code implementation • 18 Mar 2024 • Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, Jun-Yan Zhu
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 data for model fine-tuning.
2 code implementations • 6 Feb 2023 • Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, Jun-Yan Zhu
However, it is still challenging to directly apply these models for editing real images for two reasons.
Ranked #13 on Text-based Image Editing on PIE-Bench
1 code implementation • CVPR 2022 • Gaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, Krishna Kumar Singh
We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2.
3 code implementations • CVPR 2022 • Gaurav Parmar, Richard Zhang, Jun-Yan Zhu
Furthermore, we show that if compression is used on real training images, FID can actually improve if the generated images are also subsequently compressed.
no code implementations • CVPR 2021 • Gaurav Parmar, Dacheng Li, Kwonjoon Lee, Zhuowen Tu
Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive.
Ranked #2 on Image Generation on LSUN Bedroom 128 x 128
no code implementations • CVPR 2020 • Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.