Author name cluster

Zeng Zhao

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5 papers

1 author row

AAAI Conference 2025 Conference Paper

DialogDraw: Image Generation and Editing System Based on Multi-Turn Dialogue

Shichao Ma
Xinfeng Zhang
Zeng Zhao
Bai Liu
Changjie Fan
Zhipeng Hu

In recent years, diffusion modeling has shown great potential for image generation and editing. Beyond single-model approaches, various drawing workflows now exist to handle diverse drawing tasks. However, few solutions effectively identify user intentions through dialogue and progressively complete drawings. We introduce DialogDraw, which facilitates image generation and editing through continuous dialogue interaction. DialogDraw enables users to create and refine drawings using natural language and integrates with numerous open-source drawing workflows and models. The system accurately recognizes intentions and extracts user inputs via parameterization, adapts to various drawing function parameters, and provides an intuitive interaction mode. It effectively executes user instructions, supports dozens of image generation and editing methods, and offers robust scalability. Moreover, we employ SFT and RLHF to iterate the Intention Recognition and Parameter Extraction Model (IRPEM). To evaluate DialogDraw's functionality, we propose DrawnConvos, a dataset rich in drawing functions and command dialogue data collected from the open-source community. Our evaluation demonstrates that DialogDraw excels in command compliance, identifying and adapting to user drawing intentions, thereby proving the effectiveness of our method.

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AAAI Conference 2025 Conference Paper

LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation

Mushui Liu
Yuhang Ma
Zhen Yang
Jun Dan
Yunlong Yu
Zeng Zhao
Zhipeng Hu
Bai Liu

Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving multiple objects, attribute binding, and long descriptions. In this paper, we propose a novel framework called LLM4GEN, which enhances the semantic understanding of text-to-image diffusion models by leveraging the representation of Large Language Models (LLMs). It can be seamlessly incorporated into various diffusion models as a plug-and-play component. A specially designed Cross-Adapter Module (CAM) integrates the original text features of text-to-image models with LLM features, thereby enhancing text-to-image generation. Additionally, to facilitate and correct entity-attribute relationships in text prompts, we develop an entity-guided regularization loss to further improve generation performance. We also introduce DensePrompts, which contains 7,000 dense prompts to provide a comprehensive evaluation for the text-to-image generation task. Experiments indicate that LLM4GEN significantly improves the semantic alignment of SD1.5 and SDXL, demonstrating increases of 9.69% and 12.90% in color on T2I-CompBench, respectively. Moreover, it surpasses existing models in terms of sample quality, image-text alignment, and human evaluation.

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AAAI Conference 2025 Conference Paper

Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection

Yuhang Ma
Wenting Xu
Chaoyi Zhao
Keqiang Sun
Qinfeng Jin
Xiaoda Yang
Zeng Zhao
Changjie Fan

Recent advances in text-to-image diffusion models have spurred significant interest in continuous story image generation. In this paper, we introduce Storynizor, a model capable of generating coherent stories with strong inter-frame character consistency, effective foreground-background separation, and diverse pose variation. The core innovation of Storynizor lies in its key modules: ID-Synchronizer and ID-Injector. The ID-Synchronizer employs an auto-mask self-attention module and a mask perceptual loss across inter-frame images to improve the consistency of character generation, vividly representing their postures and backgrounds. The ID-Injector utilize a Shuffling Reference Strategy (SRS) to integrate ID features into specific locations, enhancing ID-based consistent character generation. Additionally, to facilitate the training of Storynizor, we have curated a novel dataset called StoryDB comprising 100, 000 images. This dataset contains single and multiple-character sets in diverse environments, layouts, and gestures with detailed descriptions. Experimental results indicate that Storynizor demonstrates superior coherent story generation with high-fidelity character consistency, flexible postures, and vivid backgrounds compared to other character-specific methods.

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AAMAS Conference 2024 Conference Paper

Mastering Robot Control through Point-based Reinforcement Learning with Pre-training

Yihong Chen
Cong Wang
Tianpei Yang
Meng Wang
Yingfeng Chen
Jifei Zhou
Chaoyi Zhao
Xinfeng Zhang

Visual-based Reinforcement Learning (RL) has gained prominence in robotics decision-making due to its significant potential. However, the prevalent utilization of images in visual-based RL lacks explicit descriptions of object structures and spatial configurations in scenes, thereby limiting the overall efficiency and robustness of RL in robot control. Additionally, training an RL policy solely using visual observations from scratch is typically sample-inefficient, rendering it impractical for real-world application. To address these challenges, this paper proposes a novel method, called Pre-training on Point-based RL (P2RL), which takes the point cloud representations of scenes as states and preserves the intricate spatial details between objects. To further enhance efficiency, we leverage the pre-training method to bolster the perception ability of the network. Key factors in the pre-training process are systematically examined to optimize downstream RL training. Experimental results demonstrate the superior robustness and efficiency of P2RL compared to the state-of-the-art image-based RL method, especially in evaluations involving untrained scenes.

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AAAI Conference 2024 Conference Paper

Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-Modal Structured Representations

Yufeng Huang
Jiji Tang
Zhuo Chen
Rongsheng Zhang
Xinfeng Zhang
Weijie Chen
Zeng Zhao
Zhou Zhao

Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require structured representations, i.e., representations of objects, attributes, and relations. The models cannot make a distinction between "An astronaut rides a horse" and "A horse rides an astronaut". This is because they fail to fully leverage structured knowledge when learning multi-modal representations. In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations. Firstly, we use scene graphs to guide the construction of semantic negative examples, which results in an increased emphasis on learning structured representations. Moreover, a Knowledge-Enhance Encoder (KEE) is proposed to leverage SGK as input to further enhance structured representations. To verify the effectiveness of the proposed framework, we pre-train our model with the aforementioned approaches and conduct experiments on downstream tasks. Experimental results demonstrate that Structure-CLIP achieves state-of-the-art (SOTA) performance on VG-Attribution and VG-Relation datasets, with 12.5% and 4.1% ahead of the multi-modal SOTA model respectively. Meanwhile, the results on MSCOCO indicate that Structure-CLIP significantly enhances the structured representations while maintaining the ability of general representations. Our code is available at https://github.com/zjukg/Structure-CLIP.

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