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Yue Ma

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Preprint Aug 2026

EchoWM: Open and Enterable Omnimodal World Models

We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music and speech. We organize interaction around camera intent: in first-person scenes, it specifies observer motion, while in third-person scenes,...

Song-Chun Zhang, Yao-Wei Li, Junhao Zhuang et al. · 5 citations · ⚡1
Preprint Sep 2026

Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing

Text-guided video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches provide strong control but demand substantial data and computation. Training-free methods fall into inversion-free and inversion-based paradigms. Inversion-free approac...

Chong-Bo Zhao, Jiangming Wang, Xilai Wang et al. · 0 citations
#robotics Preprint Aug 2026

Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models

PILOT (Physical Inference for Latent Optimized Trajectories) bridges the gap between high-level physical condition evolution and low-level action trajectory generation within the Action Model, creating a structural bottleneck while weakening the predictive capability of world evolution modeling for action generation.

Xiangkai Ma, Yue Ma, Junjie Wang et al. · 0 citations
Preprint Aug 2026

LiveLight: Real-time Streaming Video Relighting with Interactive Control

LiveLight is presented, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control, which achieves state-of-the-art relighting quality while running at real-time speed, and will publicly release its models, training data, and synthetic data generator.

Yue Ma, Jiangming Wang, Yucheng Wang et al. · 1 citation
Jul 2026

EgoGenesis: Egocentric World-Action Modeling with Online Anchored Projective Memory and Action-3D RoPE

Method, an egocentric world-action simulator that synthesizes controllable, high-quality manipulation videos to expand scarce real-world training data, is presented, demonstrating that the synthesized data substantially improve downstream WAM generalization.

Zexuan Yan, Yuzhou Wu, Yue Ma et al. · 0 citations

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