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Spotlight
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Chuanxia Zheng · Tung-Long Vuong · Jianfei Cai · Dinh Phung

Thu Dec 08 05:00 PM -- 07:00 PM (PST) @

Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures. To address this issue, we propose to incorporate the spatially conditional normalization to modulate the quantized vectors so as to insert spatially variant information to the embedded index maps, encouraging the decoder to generate more photorealistic images. Moreover, we use multichannel quantization to increase the recombination capability of the discrete codes without increasing the cost of model and codebook. Additionally, to generate discrete tokens at the second stage, we adopt a Masked Generative Image Transformer (MaskGIT) to learn an underlying prior distribution in the compressed latent space, which is much faster than the conventional autoregressive model. Experiments on two benchmark datasets demonstrate that our proposed modulated VQGAN is able to greatly improve the reconstructed image quality as well as provide high-fidelity image generation.

Author Information

Chuanxia Zheng (Monash University)
Chuanxia Zheng

I am a research fellow in the Department of Data Science & AI at Monash University. My research interests focus on computer vision and machine learning, especially for scene understanding and synthesis. I have done a wide range of research on 2D image generation, completion and translation, 3D scene reconstruction, translation and interpretation, which are applicable to the problem of synthesizing a photorealistic virtual world from images.

Tung-Long Vuong (VNU - University of Engineering and Technology)
Jianfei Cai (Monash University)
Dinh Phung (Monash University)

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