117 citations · 131 across the 9 of their papers we have counts for
9 papers
DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents
Rongsheng Zhang, Jiji Tang, Junnan Ren +6
Role-playing with large language models is fundamentally a session-level task, requiring agents to sustain character identity and interaction quality across extended multi-turn con…
From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents
Rongsheng Zhang, Ruofan Hu, Weijie Chen +7
While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely…
StoryWeaver: A Unified World Model for Knowledge-Enhanced Story Character Customization
Jinlu Zhang, Jiji Tang, Rongsheng Zhang +2
Story visualization has gained increasing attention in artificial intelligence. However, existing methods still struggle with maintaining a balance between character identity prese…
Character-Adapter: Prompt-Guided Region Control for High-Fidelity Character Customization
Yuhang Ma, Wenting Xu, Jiji Tang +5
Customized image generation, which seeks to synthesize images with consistent characters, holds significant relevance for applications such as storytelling, portrait generation, an…
Let Storytelling Tell Vivid Stories: An Expressive and Fluent Multimodal Storyteller
Chuanqi Zang, Jiji Tang, Rongsheng Zhang +4
Storytelling aims to generate reasonable and vivid narratives based on an ordered image stream. The fidelity to the image story theme and the divergence of story plots attract read…
Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks
Siyu Zou, Jiji Tang, Yiyi Zhou +5
Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most exis…
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
Haowei Wang, Jiji Tang, Jiayi Ji +8
In recent years, 3D understanding has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment s…
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
Yufeng Huang, Jiji Tang, Zhuo Chen +8
Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on i…
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
Fei Yu, Jiji Tang, Weichong Yin +4
We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL…