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cs.CV2026

WorldStream: Streaming Interactive 3D Worlds with Hierarchical Planning and Persistent State

Xiaoxiao Ma, Weiyang Jin, Yanwen Ma +11

We present WorldStream, an open-ended 3D world model with three simultaneous capabilities: it extends playable terrain beyond any pre-specified map, acts on open-ended language in…

cs.CV2026

SGF+: Decoupling Gradient Flows for Autoregressive Video Generation

Zihan Su, Junhao Zhuang, Yaowei Li +10

Autoregressive video generation requires denoising the current frames while writing their key-value representations as context for future predictions. However, these two roles typi…

cs.CV2026

Where and When to Force: Routed Forcing for Streaming Avatars

Zihan Su, Siwen Lu, Junhao Zhuang +6

Audio-driven streaming avatar generation requires real-time synthesis of speech-synchronized videos with dynamic and diverse motion. Self Forcing uses Distribution Matching Distill…

cs.CV2026

Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds

Nan Duan, Haoyang Huang, Weiyang Jin +13

Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, follow user controls, and remain…

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

Self Gradient Forcing: Native Long Video Extrapolation

Junhao Zhuang, Shiyi Zhang, Yuxuan Bian +11

Recent autoregressive video diffusion methods are increasingly built upon Self Forcing, where the student is trained on histories produced by its own rollout rather than ground-tru…

cs.CV2026

Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization

Xiaoxuan He, Siming Fu, Zeyue Xue +9

Group Relative Policy Optimization has emerged as essential for aligning video diffusion models with human preferences, but faces a critical computational bottleneck: training a 14…

cs.CV2026

Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions

Xin Lu, Luxury, Jie Huang +27

While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., 480P), leaving efficient, scalable, real-tim…

cs.CV2026

HPSv3++: Scaling Reward Models Across the Full Spectrum of Diffusion Model Capabilities

Yijun Liu, Jie Huang, Zeyue Xue +5

Reward models guide text-to-image (T2I) systems toward outputs aligned with human preferences. However, typical reward models such as HPSv3 are trained on pre-annotated data from e…

cs.CV2026

Echo-Memory: A Controlled Study of Memory in Action World Models

Wayne King, Zeyue Xue, Yuxuan Bian +13

We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text pro…

cs.CV2026

OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation

Guohui Zhang, XiaoXiao Ma, Jie Huang +9

Recent advances in joint audio-video generation have been remarkable, yet real-world applications demand strong per-modality fidelity, cross-modal alignment, and fine-grained synch…

cs.CV2026

A Systematic Post-Train Framework for Video Generation

Zeyue Xue, Siming Fu, Jie Huang +9

While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between th…

cs.CV2026

Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models

Songchun Zhang, Zeyue Xue, Siming Fu +6

Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences. Existing reinforcement learning (RL) framewo…

cs.CV2025

RewardDance: Reward Scaling in Visual Generation

Jie Wu, Yu Gao, Zilyu Ye +9

Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It pr…

cs.CV2025

WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception

Zhiheng Liu, Xueqing Deng, Shoufa Chen +7

Generative video modeling has made significant strides, yet ensuring structural and temporal consistency over long sequences remains a challenge. Current methods predominantly rely…

cs.CV2025

DanceGRPO: Unleashing GRPO on Visual Generation

Zeyue Xue, Jie Wu, Yu Gao +8

Recent advances in generative AI have revolutionized visual content creation, yet aligning model outputs with human preferences remains a critical challenge. While Reinforcement Le…