About this episode

This episode discusses the introduction of InterleaveThinker, a multi-agent pipeline for enhancing image generators with interleaved generation capabilities.

🤗 Upvotes: 73 | cs.CV Authors: Dian Zheng, Harry Lee, Manyuan Zhang, Kaituo Feng, Zoey Guo, Ray Zhang, Hongsheng Li Title: InterleaveThinker: Reinforcing Agentic Interleaved Generation Arxiv: http://arxiv.org/abs/2606.13679v1 Abstract: Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs, identify samples that deviate from the planned instructions, and refine the instructions for regeneration. To…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• image generation
• interleaved generation
• multi-agent systems
• visual narratives
• instruction-following

Keywords

• InterleaveThinker
• image generators
• multi-agent pipeline
• interleaved generation
• visual narratives
• instruction-following

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