Next Visual Granularity Generation
We propose a novel approach to image generation by decomposing an image into
a structured sequence, where each element in the sequence shares the same
spatial resolution but differs in the number of unique tokens used, capturing
different level of visual granularity. Image generation is carried out through
our newly introduced Next Visual Granularity (NVG) generation framework, which
generates a visual granularity sequence beginning from an empty image and
progressively refines it, from global layout to fine details, in a structured
manner. This iterative process encodes a hierarchical, layered representation
that offers fine-grained control over the generation process across multiple
granularity levels. We train a series of NVG models for class-conditional image
generation on the ImageNet dataset and observe clear scaling behavior. Compared
to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30
-> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to
showcase the capability and potential of the NVG framework. Our code and models
will be released.
68a3e68db65388761d0744e0
Yikai Wang
claimed_verified
2025-08-19T08:04:19.759Z
63463bc4547c70e4b7d3009f
/avatars/6e5350fd998f0a7a4143d7504218164a.svg
Yikai Wang
user
yikaiwang
68a3e68db65388761d0744e1
Zhouxia Wang
claimed_verified
2025-08-25T08:27:50.007Z
64db92a5858f8a41c11669b7
/avatars/e834d8f1d4781e3bb0b5d6d25b3b3505.svg
Zhouxia Wang
user
wzhouxiff
68a3e68db65388761d0744e2
Zhonghua Wu
68a3e68db65388761d0744e3
Qingyi Tao
68a3e68db65388761d0744e4
Kang Liao
68a3e68db65388761d0744e5
Chen Change Loy
Next Visual Granularity Generation
We propose a novel approach to image generation by decomposing an image into
a structured sequence, where each element in the sequence shares the same
spatial resolution but differs in the number of unique tokens used, capturing
different level of visual granularity. Image generation is carried out through
our newly introduced Next Visual Granularity (NVG) generation framework, which
generates a visual granularity sequence beginning from an empty image and
progressively refines it, from global layout to fine details, in a structured
manner. This iterative process encodes a hierarchical, layered representation
that offers fine-grained control over the generation process across multiple
granularity levels. We train a series of NVG models for class-conditional image
generation on the ImageNet dataset and observe clear scaling behavior. Compared
to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30
-> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to
showcase the capability and potential of the NVG framework. Our code and models
will be released.
A novel Next Visual Granularity (NVG) framework generates images by iteratively refining a sequence of visual granularities, outperforming existing methods in class-conditional image generation.
Next Visual Granularity (NVG) visual granularity sequence global layout fine details hierarchical layered representation class-conditional image generation ImageNet dataset FID scores