arXiv 2610.05416 · cs.CV · 2026-10-04
Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training
Shuyuan Tu, Qi Tian, Yinming Huang, Yue Wu, Xintong Han, Kaihang Pan, Weijie Kong, Jiangfeng Xiong and 3 more
Tencent Hunyuan
In latest · #166 Added on 2026-10-21
One-line takeaway
Prism is dynamic sparse attention that focuses on sound-producing regions for training native 2K joint video-audio models.
Prism 动态稀疏注意力聚焦发声区域,用于原生 2K 音视频联合生成模型的训练。
Read on arXiv PDF Hugging Face discussion Code Project page
Topics & disciplines
More on Visual generation
- MaLiang-Harness: A Programmable Path to Image and Video Generation ▲367
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generation ▲364
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models ▲325
- UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation ▲197
- Matrix-Game 4.0: Harnessing Video Models for Real-Time Playable Game Worlds ▲182
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-22.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.