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cs.CV2026

iFSQ: Improving FSQ for Image Generation with 1 Line of Code

Bin Lin, Zongjian Li, Yuwei Niu +9

The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, r…

cs.CV2025

JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization

Yunlong Lin, Linqing Wang, Kunjie Lin +11

Agent-based editing models have substantially advanced interactive experiences, processing quality, and creative flexibility. However, two critical challenges persist: (1) instruct…

cs.CV2025

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

Yuwei Niu, Weiyang Jin, Jiaqi Liao +7

Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this,…

cs.CV2025

Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback

Zongjian Li, Zheyuan Liu, Qihui Zhang +10

Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their abi…

cs.CV2025

GIR-Bench: Versatile Benchmark for Generating Images with Reasoning

Hongxiang Li, Yaowei Li, Bin Lin +7

Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal inte…

cs.CV2025

Unified Multimodal Models as Auto-Encoders

Zhiyuan Yan, Kaiqing Lin, Zongjian Li +10

Image-to-text (I2T) understanding and text-to-image (T2I) generation are two fundamental, important yet traditionally isolated multimodal tasks. Despite their intrinsic connection,…