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cs.CV2026

FlowAct-R2: Beyond Talking Avatar via Streaming Multimodal References and Proactive Agent Planning

Ziyao Huang, Zhengkun Rong, Shiyang Qin +5

We present FlowAct-R2, a framework for interactive humanoid video generation that combines continuous multimodal control with proactive agent planning. Our method consists of two c…

cs.CV2026

Beyond the Eye: Efficient Multimodal Reasoning via Self-Regulated Implicit Visual Tools

Xiuwei Chen, Quanlin Chen, Wentao Hu +8

Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained perception tasks under the "Thinking with Images" (TwI) paradigm by iteratively perfor…

cs.CV2026

Latent Visual States for Efficient Multimodal Reasoning

Xiuwei Chen, Wentao Hu, Yongxin Wang +8

The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete out…

cs.CV2026

WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark

Wang Lin, Feng Wang, Majun Zhang +7

Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synth…

cs.CV2025

Kling-Avatar: Grounding Multimodal Instructions for Cascaded Long-Duration Avatar Animation Synthesis

Yikang Ding, Jiwen Liu, Wenyuan Zhang +11

Recent advances in audio-driven avatar video generation have significantly enhanced audio-visual realism. However, existing methods treat instruction conditioning merely as low-lev…

cs.CV2025

VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction

Hao Wang, Eiki Murata, Lingfang Zhang +11

Recent advances in multimodal large language models (MLLMs) have significantly enhanced video understanding capabilities, opening new possibilities for practical applications. Yet…

cs.CV2025

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

Xiuwei Chen, Wentao Hu, Hanhui Li +9

Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities. However, further enhancing existing MLLMs necessitates high-quality vision…

cs.CV2025

Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning

Bohan Wang, Zhongqi Yue, Fengda Zhang +15

We completely discard the conventional spatial prior in image representation and introduce a novel discrete visual tokenizer: Self-consistency Tokenizer (Selftok). At its design co…

cs.CV2025

On Path to Multimodal Generalist: General-Level and General-Bench

Hao Fei, Yuan Zhou, Juncheng Li +29

The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…

cs.CV2025

Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning

Wang Lin, Liyu Jia, Wentao Hu +6

Despite recent progress in video generation, producing videos that adhere to physical laws remains a significant challenge. Traditional diffusion-based methods struggle to extrapol…