16 papers
CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds
Peng Zheng, Ruiqi Liu, Rui Ma +1
Recent subject-to-image models have achieved impressive progress in personalized image generation, yet they still struggle to preserve fine-grained subject-specific details. A majo…
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Yitong Chen, Zijie Diao, Junke Wang +5
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…
OmniGen-AR: AutoRegressive Any-to-Image Generation
Junke Wang, Xun Wang, Qiushan Guo +4
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, ex…
DisCo: World Models with Discrete Camera Motion Control
Hongrui Huang, Junke Wang, Quanhao Li +2
Controllable video world models target interactive world exploration, where models must faithfully execute explicit action commands while preserving visual quality and temporal coh…
Channel-wise Vector Quantization
Wei Song, Tianhang Wang, Yitong Chen +5
We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantiza…