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cs.CV2026

Motion4Motion: Motion Transfer Across Subjects at Inference

Ling-Hao Chen, Zixin Yin, Duomin Wang +2

The paper introduces Motion4Motion, a training‑free framework that transfers motion between videos by modeling motion flow instead of relying on predefined skeletons, enabling tran…

cs.CV2026

M4V: Multimodal Mamba for Efficient Text-to-Video Generation

Jiancheng Huang, Gengwei Zhang, Zequn Jie +5

Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators. However, modeling the vast spatiotemporal spa…

cs.CV2026

Simulating the Real World: A Unified Survey of Multimodal Generative Models

Yuqi Hu, Longguang Wang, Xian Liu +7

Understanding and replicating the real world is a critical challenge in Artificial General Intelligence (AGI) research. To achieve this, many existing approaches, such as world mod…

cs.CV2025

SplatFont3D: Structure-Aware Text-to-3D Artistic Font Generation with Part-Level Style Control

Ji Gan, Lingxu Chen, Jiaxu Leng +1

Artistic font generation (AFG) can assist human designers in creating innovative artistic fonts. However, most previous studies primarily focus on 2D artistic fonts in flat design,…

cs.CV2025

ConsistEdit: Highly Consistent and Precise Training-free Visual Editing

Zixin Yin, Ling-Hao Chen, Lionel Ni +1

Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current app…

cs.CV2025

UniVerse-1: Unified Audio-Video Generation via Stitching of Experts

Duomin Wang, Wei Zuo, Aojie Li +7

We introduce UniVerse-1, a unified, Veo-3-like model capable of simultaneously generating coordinated audio and video. To enhance training efficiency, we bypass training from scrat…