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cs.CV2026

EchoWM: Open and Enterable Omnimodal World Models

Songchun Zhang, Yaowei Li, Junhao Zhuang +19

We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music an…

cs.CV2026

Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency

Zihan Su, Teng Hu, Jiangning Zhang +4

Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis,…

cs.CV2026

Evolution of Video Generative Foundations

Teng Hu, Jiangning Zhang, Hongrui Huang +7

The rapid advancement of Artificial Intelligence Generated Content (AIGC) has revolutionized video generation, enabling systems ranging from proprietary pioneers like OpenAI's Sora…

cs.CV2025

PoseAnything: Universal Pose-guided Video Generation with Part-aware Temporal Coherence

Ruiyan Wang, Teng Hu, Kaihui Huang +3

Pose-guided video generation refers to controlling the motion of subjects in generated video through a sequence of poses. It enables precise control over subject motion and has imp…

cs.CV2025

Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy

Teng Hu, Zhentao Yu, Guozhen Zhang +6

The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reve…

cs.CV2025

UltraGen: High-Resolution Video Generation with Hierarchical Attention

Teng Hu, Jiangning Zhang, Zihan Su +1

Recent advances in video generation have made it possible to produce visually compelling videos, with wide-ranging applications in content creation, entertainment, and virtual real…