Yan: Foundational Interactive Video Generation
- URL: http://arxiv.org/abs/2508.08601v3
- Date: Thu, 14 Aug 2025 10:26:51 GMT
- Title: Yan: Foundational Interactive Video Generation
- Authors: Deheng Ye, Fangyun Zhou, Jiacheng Lv, Jianqi Ma, Jun Zhang, Junyan Lv, Junyou Li, Minwen Deng, Mingyu Yang, Qiang Fu, Wei Yang, Wenkai Lv, Yangbin Yu, Yewen Wang, Yonghang Guan, Zhihao Hu, Zhongbin Fang, Zhongqian Sun,
- Abstract summary: Yan is a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing.<n>We design a highly-compressed, low-latency 3D-VAE coupled with a KV-cache-based shift-window denoising inference process.<n>We propose a hybrid model that explicitly disentangles interactive mechanics simulation from visual rendering, enabling multi-granularity video content editing during interaction through text.
- Score: 25.398980906541524
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present Yan, a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing. Specifically, Yan comprises three core modules. AAA-level Simulation: We design a highly-compressed, low-latency 3D-VAE coupled with a KV-cache-based shift-window denoising inference process, achieving real-time 1080P/60FPS interactive simulation. Multi-Modal Generation: We introduce a hierarchical autoregressive caption method that injects game-specific knowledge into open-domain multi-modal video diffusion models (VDMs), then transforming the VDM into a frame-wise, action-controllable, real-time infinite interactive video generator. Notably, when the textual and visual prompts are sourced from different domains, the model demonstrates strong generalization, allowing it to blend and compose the style and mechanics across domains flexibly according to user prompts. Multi-Granularity Editing: We propose a hybrid model that explicitly disentangles interactive mechanics simulation from visual rendering, enabling multi-granularity video content editing during interaction through text. Collectively, Yan offers an integration of these modules, pushing interactive video generation beyond isolated capabilities toward a comprehensive AI-driven interactive creation paradigm, paving the way for the next generation of creative tools, media, and entertainment. The project page is: https://greatx3.github.io/Yan/.
Related papers
- VINO: A Unified Visual Generator with Interleaved OmniModal Context [36.71641694179164]
VINO is a unified visual generator that performs image and video generation and editing within a single framework.<n>Instead of relying on task-specific models or independent modules for each modality, VINO uses a shared diffusion backbone.
arXiv Detail & Related papers (2026-01-05T18:56:34Z) - Hunyuan-GameCraft-2: Instruction-following Interactive Game World Model [19.937724706042804]
Hunyuan-GameCraft-2 is a new paradigm of instruction-driven interaction for generative game world modeling.<n>Our model allows users to control game video contents through natural language prompts, keyboard, or mouse signals.<n>Our model generates temporally coherent and causally grounded interactive game videos.
arXiv Detail & Related papers (2025-11-28T18:26:39Z) - UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist [107.04196084992907]
We introduce UniVA, an omni-capable multi-agent framework for next-generation video generalists.<n>UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow.<n>We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation.
arXiv Detail & Related papers (2025-11-11T17:58:13Z) - BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration [56.98981194478512]
We propose a unified framework that handles a broad range of subject-to-video scenarios.<n>We introduce an MLLM-DiT framework in which a pretrained multimodal large language model performs deep cross-modal reasoning to ground entities.<n>Experiments on the OpenS2V benchmark demonstrate that our method achieves superior performance across subject consistency, naturalness, and text relevance in generated videos.
arXiv Detail & Related papers (2025-10-01T02:41:11Z) - Matrix-Game 2.0: An Open-Source, Real-Time, and Streaming Interactive World Model [15.16063778402193]
Matrix-Game 2.0 is an interactive world model generates long videos on-the-fly via few-step auto-regressive diffusion.<n>It can generate high-quality minute-level videos across diverse scenes at an ultra-fast speed of 25 FPS.
arXiv Detail & Related papers (2025-08-18T15:28:53Z) - Yume: An Interactive World Generation Model [38.818537395166835]
Yume aims to use images, text, or videos to create an interactive, realistic, and dynamic world.<n>Method creates a dynamic world from an input image and allows exploration of the world using keyboard actions.
arXiv Detail & Related papers (2025-07-23T17:57:09Z) - PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement [26.89021788485701]
PolyVivid is a multi-subject video customization framework that enables flexible and identity-consistent generation.<n>In experiments, PolyVivid achieves superior performance in identity fidelity, video realism, and subject alignment, outperforming existing open-source and commercial baselines.
arXiv Detail & Related papers (2025-06-09T15:11:09Z) - SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios [48.09735396455107]
Hand-Object Interaction (HOI) generation has significant application potential.<n>Current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data.<n>We propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously.
arXiv Detail & Related papers (2025-06-03T05:04:29Z) - BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations [82.94002870060045]
Existing video generation models struggle to follow complex text prompts and synthesize multiple objects.<n>We develop a blob-grounded video diffusion model named BlobGEN-Vid that allows users to control object motions and fine-grained object appearance.<n>We show that our framework is model-agnostic and build BlobGEN-Vid based on both U-Net and DiT-based video diffusion models.
arXiv Detail & Related papers (2025-01-13T19:17:06Z) - DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation [54.30327187663316]
DiTCtrl is a training-free multi-prompt video generation method under MM-DiT architectures for the first time.<n>We analyze MM-DiT's attention mechanism, finding that the 3D full attention behaves similarly to that of the cross/self-attention blocks in the UNet-like diffusion models.<n>Based on our careful design, the video generated by DiTCtrl achieves smooth transitions and consistent object motion given multiple sequential prompts.
arXiv Detail & Related papers (2024-12-24T18:51:19Z) - VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [70.61101071902596]
Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines.<n>We introduce VideoGen-of-Thought (VGoT), a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT generates multi-shot videos that outperform state-of-the-art baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
arXiv Detail & Related papers (2024-12-03T08:33:50Z) - VX2TEXT: End-to-End Learning of Video-Based Text Generation From
Multimodal Inputs [103.99315770490163]
We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio.
Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
arXiv Detail & Related papers (2021-01-28T15:22:36Z) - Dynamic Graph Representation Learning for Video Dialog via Multi-Modal
Shuffled Transformers [89.00926092864368]
We present a semantics-controlled multi-modal shuffled Transformer reasoning framework for the audio-visual scene aware dialog task.
We also present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing-semantic graph representations for every frame.
Our results demonstrate state-of-the-art performances on all evaluation metrics.
arXiv Detail & Related papers (2020-07-08T02:00:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.