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cs.CV2026

Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasoners

Qingyang Liu, Bingjie Gao, Canmiao Fu +9

Recent unified models integrate multimodal understanding and generation within a single framework. However, an "understanding-generation gap" persists, where models can capture use…

cs.CV2026

RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling

Bingjie Gao, Qianli Ma, Xiaoxue Wu +9

Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the gener…

cs.CV2026

Reflection Generation for Composite Image Using Diffusion Model

Haonan Zhao, Qingyang Liu, Jiaxuan Chen +1

Image composition involves inserting a foreground object into the background while synthesizing environment-consistent effects such as shadows and reflections. Although shadow gene…

cs.CV2026

AnimateScene: Camera-controllable Animation in Any Scene

Qingyang Liu, Bingjie Gao, Weiheng Huang +10

Recent advances in 3D scene reconstruction and 4D human animation have broadened adoption, but integrating the two remains difficult. Key challenges include placing humans at plaus…

cs.CV2025

TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models

Harold Haodong Chen, Disen Lan, Wen-Jie Shu +10

The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consi…

cs.CV2025

HyperET: Efficient Training in Hyperbolic Space for Multi-modal Large Language Models

Zelin Peng, Zhengqin Xu, Qingyang Liu +2

Multi-modal large language models (MLLMs) have emerged as a transformative approach for aligning visual and textual understanding. They typically require extremely high computation…