The Year in AI Papers
The year/Topics/Video generation and world models

Research collection

Video generation and world models

Generating or simulating video: long-form and interactive video generation, controllable video synthesis, and generative video world models.

Papers
106
Research labs
3
Official code
82

51–100 of 106 papers in this collection

51

arXiv.org

MosaicMem: Hybrid Spatial Memory for Controllable Video World Models

MosaicMem is a hybrid spatial memory mechanism for video world models that combines explicit 3D structure with implicit, attention-based conditioning. It lifts video patches into 3D for precise localization and retrieval, then composes them in the queried view via a patch-and-compose interface, allowing the model to preserve persistent elements while…

Wei Yu, Runjia Qian, Yumeng Li, Liquan Wang, et al.
Published
Mar 2026
Citations
10
Code
Not linked
Read summary Original
52

arXiv.org

WorldCam: Interactive Autoregressive 3D Gaming Worlds with Camera Pose as a Unifying Geometric Representation

WorldCam is an interactive 3D gaming world model that uses camera pose as a unifying geometric representation to achieve precise action control, long-horizon generation, and 3D consistency. It maps user actions (keyboard/mouse) to 6-DoF camera poses via Lie algebra, injecting them into a video DiT (Wan-2.1) for accurate control. Global camera poses serve…

Jisu Nam, Yicong Hong, Chun-Hao Paul Huang, Feng Liu, et al.
Published
Mar 2026
Citations
6
Code
179 stars
Read summary Original
53

arXiv.org

Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation

Kinema4D is a new action-conditioned 4D generative robotic simulator that models robot-world interactions as 4D spatiotemporal events. It disentangles simulation into precise 4D robot control via URDF-based kinematics and generative 4D environmental reactions using a diffusion transformer that synthesizes synchronized RGB and pointmap sequences. The…

Mutian Xu, Tianbao Zhang, Tianqi Liu, Zhaoxi Chen, et al.
Published
Mar 2026
Citations
6
Code
83 stars
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54

arXiv.org

Grounding World Simulation Models in a Real-World Metropolis

The paper introduces Seoul World Model (SWM), a city-scale world model that grounds autoregressive video generation in the real city of Seoul via retrieval-augmented conditioning on street-view images. SWM fine-tunes Cosmos-Predict2.5-2B on 440k Seoul street-view images, real driving videos, and synthetic urban data. It addresses three challenges: temporal…

Junyoung Seo, Hyunwook Choi, Minkyung Kwon, Jinhyeok Choi, et al.
Published
Mar 2026
Citations
6
Code
624 stars
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55

arXiv.org

Helios: Real Real-Time Long Video Generation Model

Helios is a 14B autoregressive diffusion model for video generation that achieves 19.5 FPS on a single NVIDIA H100 GPU, enabling minute-scale generation without common anti-drifting heuristics (e.g., self-forcing, error-banks) or standard acceleration techniques (e.g., KV-cache, quantization). It unifies T2V, I2V, and V2V tasks via a unified input…

Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, et al.
Published
Mar 2026
Citations
33
Code
2K stars
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56

arXiv.org

OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens

OmniLottie is a versatile framework for generating high-quality vector animations from multi-modal instructions (text, image, and video). It addresses the challenge of raw Lottie JSON files containing extensive invariant structural metadata by introducing a Lottie tokenizer that converts JSON into compact sequences of commands and parameters. This…

Yiying Yang, Wei Cheng, Sijin Chen, Honghao Fu, et al.
Published
Mar 2026
Citations
0
Code
732 stars
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57

arXiv.org

The Trinity of Consistency as a Defining Principle for General World Models

This paper proposes that a General World Model must be grounded in the Trinity of Consistency: Modal Consistency (semantic interface), Spatial Consistency (geometric basis), and Temporal Consistency (causal engine). The authors systematically review the evolution of multimodal learning from specialized modules to unified architectures, arguing that…

Jingxuan Wei, Siyuan Li, Yuhang Xu, Zheng Sun, et al.
Published
Feb 2026
Citations
4
Code
Not linked
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58

arXiv.org

SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model

SkyReels-V4 is a unified multi-modal video foundation model for joint video-audio generation, inpainting, and editing. It uses a dual-stream Multimodal Diffusion Transformer (MMDiT) architecture with separate video and audio branches sharing a Multimodal Large Language Model (MLLM) text encoder. The model accepts text, images, video clips, masks, and audio…

Guibin Chen, Dixuan Lin, Jiangping Yang, Youqiang Zhang, et al.
Published
Feb 2026
Citations
6
Code
Not linked
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59

arXiv.org

A Very Big Video Reasoning Suite

The paper introduces the Very Big Video Reasoning (VBVR) suite, comprising a large-scale dataset and benchmark for video reasoning. VBVR-Dataset contains 2,015,000 images and 1,007,500 video clips across 200 tasks, organized by five cognitive faculties (Abstraction, Knowledge, Perception, Spatiality, Transformation), and is approximately 1000x larger than…

Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, et al.
Published
Feb 2026
Citations
19
Code
225 stars
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60

arXiv.org

MOVA: Towards Scalable and Synchronized Video-Audio Generation

MOVA is an open-source model for generating synchronized video and audio, including lip-synced speech, sound effects, and music. It uses a Mixture-of-Experts architecture with 32B total parameters (18B active) and supports Image-Text to Video-Audio generation. The model couples a pretrained A14B video DiT and a 1.3B audio DiT via a 2.6B bidirectional…

SII-OpenMOSS Team, :, Donghua Yu, Mingshu Chen, et al.
Published
Feb 2026
Citations
29
Code
1.1K stars
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61

arXiv.org

3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation

The paper introduces 3DiMo, a framework for 3D-aware human motion control in video generation. It addresses limitations of existing methods that use 2D poses (viewpoint-bound) or explicit 3D models like SMPL (inaccurate, overriding generator priors). 3DiMo jointly trains a Transformer-based motion encoder with a pretrained DiT video generator to distill…

Zhixue Fang, Xu He, Songlin Tang, Haoxian Zhang, et al.
Published
Feb 2026
Citations
3
Code
Not linked
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62

arXiv.org

Advancing Open-source World Models

LingBot-World is an open-source world simulator built from video generation, offering high-fidelity dynamics across diverse environments, minute-level long-term memory, and real-time interactivity with sub-second latency. It uses a three-stage training pipeline: pre-training on a 14B-parameter Wan2.2 model, middle-training with a mixture-of-experts (MoE)…

Robbyant Team, Zelin Gao, Qiuyu Wang, Yanhong Zeng, et al.
Published
Jan 2026
Citations
87
Code
4.3K stars
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63

arXiv.org

The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation

This paper introduces an agentic framework for generating long-horizon cinematic videos from dialogue. The framework comprises three agents: ScripterAgent, which translates dialogue into detailed cinematic scripts; DirectorAgent, which orchestrates video generation using a cross-scene continuous generation strategy with frame-anchoring to ensure temporal…

Chenyu Mu, Xin He, Qu Yang, Wanshun Chen, et al.
Published
Jan 2026
Citations
3
Code
Not linked
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64

arXiv.org

FlowAct-R1: Towards Interactive Humanoid Video Generation

FlowAct-R1 is a framework for real-time interactive humanoid video generation, built on a MMDiT architecture (Seedance). It enables streaming synthesis of arbitrary-length videos with low latency, achieving 25fps at 480p resolution with a time-to-first-frame of about 1.5 seconds. The method uses a chunkwise diffusion forcing strategy with a self-forcing…

Lizhen Wang, Yongming Zhu, Zhipeng Ge, Youwei Zheng, et al.
Published
Jan 2026
Citations
7
Code
Not linked
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65

NVIDIA

Motion Attribution for Video Generation

The paper introduces Motive, a gradient-based data attribution framework for video generation models that isolates motion from static appearance. It uses motion-weighted loss masks, computed via AllTracker optical flow, to focus influence scores on dynamic regions. Motive scales via single-timestep estimation, common randomness, frame-length normalization,…

Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, et al.
Published
Jan 2026
Citations
2
Code
Not linked
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66

arXiv.org

LTX-2: Efficient Joint Audio-Visual Foundation Model

LTX-2 is an open-source text-to-audio+video (T2AV) foundation model that jointly generates synchronized video and audio from text. It uses an asymmetric dual-stream transformer with a 14B-parameter video stream and a 5B-parameter audio stream, connected via bidirectional cross-attention layers with temporal positional embeddings and cross-modality AdaLN.…

Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, et al.
Published
Jan 2026
Citations
135
Code
8.5K stars
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67

arXiv.org

DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

The paper introduces DreamID-V, a Diffusion Transformer (DiT)-based framework for high-fidelity video face swapping (VFS). It addresses the gap between image face swapping (IFS) and VFS by proposing a data pipeline, SyncID-Pipe, which pre-trains an Identity-Anchored Video Synthesizer (IVS) to generate synthetic videos, combined with IFS models to create…

Xu Guo, Fulong Ye, Xinghui Li, Pengqi Tu, et al.
Published
Jan 2026
Citations
7
Code
668 stars
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68

arXiv.org

Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation

Avatar Forcing is a framework for real-time interactive head avatar generation that models user-avatar interactions using diffusion forcing. It addresses two key challenges: real-time motion generation under causal constraints and learning expressive reactions without labeled data. The framework processes multimodal user inputs (audio and motion) with low…

Taekyung Ki, Sangwon Jang, Jaehyeong Jo, Jaehong Yoon, et al.
Published
Jan 2026
Citations
13
Code
342 stars
Read summary Original
69

arXiv.org

NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos

NeoVerse is a 4D world model that reconstructs dynamic 4D Gaussian Splatting (4DGS) from monocular videos in a feed-forward, pose-free manner, enabling novel-trajectory video generation and downstream applications. It addresses scalability limitations of prior methods by avoiding expensive multi-view data and offline preprocessing. Key innovations include…

Yuxue Yang, Lue Fan, Ziqi Shi, Junran Peng, et al.
Published
Jan 2026
Citations
28
Code
647 stars
Read summary Original
70

arXiv.org

LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation

The paper introduces LiveTalk, a real-time multimodal interactive video diffusion system. It addresses the high inference cost of diffusion models by distilling a bidirectional, many-step model into a causal, 4-step autoregressive one. The authors identify that the leading on-policy distillation method, Self Forcing, suffers from training instability and…

Ethan Chern, Zhulin Hu, Bohao Tang, Jiadi Su, et al.
Published
Dec 2025
Citations
7
Code
331 stars
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71

arXiv.org

Yume-1.5: A Text-Controlled Interactive World Generation Model

Yume1.5 is a framework for generating interactive, continuous virtual worlds from a single image or text prompt, with keyboard-based control for person and camera movement. It addresses limitations in existing video diffusion models, such as limited generalizability, high latency, and insufficient text control. The framework introduces three core…

Xiaofeng Mao, Zhen Li, Chuanhao Li, Xiaojie Xu, et al.
Published
Dec 2025
Citations
52
Code
681 stars
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72

arXiv.org

SemanticGen: Video Generation in Semantic Space

SemanticGen is a novel video generation framework that operates in a compact semantic space rather than directly in the VAE latent space. It uses a two-stage process: first, a diffusion model generates compressed semantic video features (using Qwen-2.5-VL as the semantic encoder) that define the global layout; second, another diffusion model generates VAE…

Jianhong Bai, Xiaoshi Wu, Xintao Wang, Xiao Fu, et al.
Published
Dec 2025
Citations
7
Code
Not linked
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73

Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers

MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation

MACE-Dance is a music-driven dance video generation framework using a cascaded Mixture-of-Experts (MoE) design, decoupling the task into a Motion Expert and an Appearance Expert. The Motion Expert generates 3D SMPL motion from music using a Diffusion Model with a BiMamba-Transformer hybrid architecture and Guidance-Free Training (GFT), achieving…

Kaixing Yang, Jiashu Zhu, Xulong Tang, Ziqiao Peng, et al.
Published
Dec 2025
Citations
9
Code
108 stars
Read summary Original
74

arXiv.org

Region-Constraint In-Context Generation for Instructional Video Editing

ReCo is a novel framework for instruction-based video editing that uses in-context generation with region constraints. It concatenates source and target videos for joint denoising and introduces two regularization terms: latent-space regularization increases latent discrepancy in editing regions while reducing it in non-editing areas, and attention-space…

Zhongwei Zhang, Fuchen Long, Wei Li, Zhaofan Qiu, et al.
Published
Dec 2025
Citations
14
Code
174 stars
Read summary Original
75

Independent research

InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion

InsertAnywhere is a framework for video object insertion (VOI) that addresses limitations in 4D scene understanding and optical effects. It uses a two-stage pipeline: first, a 4D-aware mask generation module reconstructs the video into a 4D scene, allowing users to anchor an object's 3D pose in one frame, then propagates it via scene flow tracking to…

Hoiyeong Jin, Hyojin Jang, Junha Hyung, Jeongho Kim, et al.
Published
Dec 2025
Citations
0
Code
89 stars
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76

arXiv.org

Kling-Omni Technical Report

Kling-Omni is a generalist generative framework from Kuaishou Technology that unifies video generation, editing, and reasoning into a single end-to-end system. It introduces Multi-modal Visual Language (MVL) as an interaction paradigm, combining text, images, and videos into a unified representation. The architecture includes a Prompt Enhancer (PE) based…

Kling Team, Jialu Chen, Yuanzheng Ci, Xiangyu Du, et al.
Published
Dec 2025
Citations
41
Code
Not linked
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77

arXiv.org

TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times

TurboDiffusion is a video generation acceleration framework that achieves 100–200× end-to-end speedup while maintaining video quality. It combines four main techniques: low-bit SageAttention for attention acceleration, Sparse-Linear Attention (SLA) for sparse attention, rCM for step distillation, and W8A8 quantization for linear layers. Training involves…

Jintao Zhang, Kaiwen Zheng, Kai Jiang, Haoxu Wang, et al.
Published
Dec 2025
Citations
28
Code
3.6K stars
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78

arXiv.org

WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling

WorldPlay is a real-time interactive world model that generates streaming 720p video at 24 FPS while maintaining long-term geometric consistency. It addresses the trade-off between speed and memory in existing methods. The model uses three key components: Dual Action Representation combining discrete keys and continuous camera poses for robust control;…

Wenqiang Sun, Haiyu Zhang, Haoyuan Wang, Junta Wu, et al.
Published
Dec 2025
Citations
97
Code
1.6K stars
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79

arXiv.org

LongVie 2: Multimodal Controllable Ultra-Long Video World Model

LongVie 2 is an end-to-end autoregressive framework for controllable ultra-long video generation, extending pretrained diffusion backbones (Wan2.1-I2V-14B) into a video world model. It is trained in three progressive stages: (1) multi-modal guidance integrating dense (depth maps) and sparse (point maps) control signals via a ControlNet-style architecture…

Jianxiong Gao, Zhaoxi Chen, Xian Liu, Junhao Zhuang, et al.
Published
Dec 2025
Citations
6
Code
335 stars
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80

arXiv.org

StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation

StereoWorld is an end-to-end diffusion-based framework that converts monocular videos into high-fidelity stereo videos by adapting a pretrained video generator. It conditions the model on the left-view video and uses a geometry-aware regularization combining disparity and depth supervision to ensure 3D structural fidelity. A spatio-temporal tiling scheme…

Ke Xing, Xiaojie Jin, Longfei Li, Yuyang Yin, et al.
Published
Dec 2025
Citations
2
Code
Not linked
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81

arXiv.org

Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance

Wan-Move is a framework for motion-controllable video generation that enhances existing image-to-video (I2V) models without adding auxiliary modules. It represents object motion using dense point trajectories, which are transferred into latent space and used to replicate first-frame features along each trajectory, creating a motion-aware condition feature…

Ruihang Chu, Yefei He, Zhekai Chen, Shiwei Zhang, et al.
Published
Dec 2025
Citations
37
Code
650 stars
Read summary Original
82

arXiv.org

EgoX: Egocentric Video Generation from a Single Exocentric Video

EgoX is a novel framework that generates egocentric (first-person) videos from a single exocentric (third-person) video input. It leverages a pretrained video diffusion model (Wan 2.1 14B) with lightweight LoRA adaptation, avoiding the need for additional inputs like multiple views or initial frames. The method lifts the exocentric video into a 3D point…

Taewoong Kang, Kinam Kim, Dohyeon Kim, Minho Park, et al.
Published
Dec 2025
Citations
6
Code
741 stars
Read summary Original
83

arXiv.org

Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

Live Avatar is an algorithm-system co-designed framework enabling real-time, streaming, and infinite-length audio-driven avatar generation using a 14-billion-parameter diffusion model. It addresses two key challenges: long-horizon consistency and the real-time-fidelity trade-off. The algorithm side uses a two-stage pipeline (Diffusion Forcing pretraining…

Yubo Huang, Hailong Guo, Fangtai Wu, Weiqiang Wang, et al.
Published
Dec 2025
Citations
0
Code
2.3K stars
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84

arXiv.org

MultiShotMaster: A Controllable Multi-Shot Video Generation Framework

MultiShotMaster is a framework for controllable multi-shot video generation, extending a pretrained single-shot text-to-video model. It introduces two RoPE variants: Multi-Shot Narrative RoPE, which applies phase shifts at shot boundaries for flexible shot arrangement while preserving narrative order, and Spatiotemporal Position-Aware RoPE, which enables…

Qinghe Wang, Xiaoyu Shi, Baolu Li, Weikang Bian, et al.
Published
Dec 2025
Citations
28
Code
174 stars
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85

arXiv.org

What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewards

The paper introduces NewtonRewards, a physics-grounded post-training framework for video generation that enforces Newton's laws of motion using verifiable rewards. It addresses the issue that video diffusion models often produce visually realistic but physically implausible motion. The method uses optical flow as a proxy for velocity and visual features…

Minh-Quan Le, Yuanzhi Zhu, Vicky Kalogeiton, Dimitris Samaras
Published
Nov 2025
Citations
18
Code
16 stars
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86

arXiv.org

First Frame Is the Place to Go for Video Content Customization

This paper introduces FFGo, a lightweight add-on for video generation models that enables multi-reference video content customization without architectural changes or large-scale fine-tuning. The authors discover that pre-trained video models treat the first frame as a conceptual memory buffer, storing visual entities for later reuse. FFGo leverages this…

Jingxi Chen, Zongxia Li, Zhichao Liu, Guangyao Shi, et al.
Published
Nov 2025
Citations
8
Code
194 stars
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87

arXiv.org

PAN: A World Model for General, Interactable, and Long-Horizon World Simulation

PAN is a general, interactable, and long-horizon world model that predicts future world states via video simulation conditioned on history and natural language actions. It uses the Generative Latent Prediction (GLP) architecture, combining an autoregressive LLM-based latent dynamics backbone (Qwen2.5-VL-7B) with a video diffusion decoder (Wan2.1-T2V-14B)…

PAN Team, Jiannan Xiang, Yi Gu, Zihan Liu, et al.
Published
Nov 2025
Citations
33
Code
Not linked
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88

arXiv.org

Time-to-Move: Training-Free Motion Controlled Video Generation via Dual-Clock Denoising

Time-to-Move (TTM) is a training-free, plug-and-play framework for motion- and appearance-controlled video generation using image-to-video (I2V) diffusion models. It uses crude reference animations (e.g., cut-and-drag or depth-based reprojection) as motion cues, adapting SDEdit's noise injection to video. To preserve appearance, it anchors generation to…

Assaf Singer, Noam Rotstein, Amir Mann, Ron Kimmel, et al.
Published
Nov 2025
Citations
11
Code
370 stars
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89

arXiv.org

Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm

The paper proposes 'Thinking with Video', a new paradigm using video generation models like Sora-2 for multimodal reasoning, addressing limitations of text- and image-based paradigms. The authors introduce VideoThinkBench, a benchmark with vision-centric tasks (eyeballing puzzles, visual puzzles, ARC-AGI-2, mazes) and text-centric tasks (GSM8K, MATH, MMLU,…

Jingqi Tong, Yurong Mou, Hangcheng Li, Mingzhe Li, et al.
Published
Nov 2025
Citations
27
Code
316 stars
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90

arXiv.org

UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions

UniAVGen is a unified framework for human-centric joint audio and video generation, addressing limitations in existing methods like poor lip synchronization and semantic inconsistency. It uses a dual-branch architecture with two parallel Diffusion Transformers (DiTs) for video and audio, enabling a cohesive cross-modal latent space. The core innovation is…

Guozhen Zhang, Zixiang Zhou, Teng Hu, Ziqiao Peng, et al.
Published
Nov 2025
Citations
28
Code
57 stars
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91

arXiv.org

World-in-World: World Models in a Closed-Loop World

The paper introduces World-in-World, the first open benchmark for evaluating generative world models (WMs) in a closed-loop embodied setting, moving beyond visual quality to task success. It provides a unified online planning strategy and a standardized action API to integrate diverse WMs into four embodied tasks: Active Recognition, Image-Goal Navigation,…

Jiahan Zhang, Muqing Jiang, Nanru Dai, Taiming Lu, et al.
Published
Oct 2025
Citations
35
Code
181 stars
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92

AAAI Conference on Artificial Intelligence

ImagerySearch: Adaptive Test-Time Search for Video Generation Beyond Semantic Dependency Constraints

The paper introduces ImagerySearch, a test-time search strategy for text-to-video generation that adapts to prompts with long-distance semantic relationships, which are rare in training data and cause performance degradation in imaginative scenarios. ImagerySearch dynamically adjusts the inference search space (SaDSS) and reward function (AIR) based on the…

Meiqi Wu, Jiashu Zhu, Xiaokun Feng, Chubin Chen, et al.
Published
Oct 2025
Citations
12
Code
56 stars
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93

arXiv.org

VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning

VideoCanvas is a unified framework for arbitrary spatio-temporal video completion, where users specify patches at any spatial location and timestamp, and the model generates a coherent video. The paper identifies that causal video VAEs compress multiple frames into a single latent slot, creating temporal ambiguity, and that zero-padding in video mode…

Minghong Cai, Qiulin Wang, Zongli Ye, Wenze Liu, et al.
Published
Oct 2025
Citations
4
Code
68 stars
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94

arXiv.org

Paper2Video: Automatic Video Generation from Scientific Papers

The paper introduces Paper2Video, the first benchmark of 101 research papers paired with author-created presentation videos, slides, and speaker metadata, along with four evaluation metrics: Meta Similarity, PresentArena, PresentQuiz, and IP Memory. It also proposes PaperTalker, a multi-agent framework that generates presentation videos from papers,…

Zeyu Zhu, Kevin Qinghong Lin, Mike Zheng Shou
Published
Oct 2025
Citations
24
Code
2.3K stars
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95

arXiv.org

Self-Forcing++: Towards Minute-Scale High-Quality Video Generation

Self-Forcing++ is a method for long-horizon video generation that extends autoregressive diffusion models beyond the training horizon of their teacher models. It addresses quality degradation from error accumulation by generating long self-rollouts (up to 100 seconds), re-injecting noise into these degraded sequences (backward noise initialization), and…

Justin Cui, Jie Wu, Ming Li, Tao Yang, et al.
Published
Oct 2025
Citations
156
Code
268 stars
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96

NVIDIA

SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer

SANA-Video is a small diffusion model for efficient, high-resolution (up to 720×1280) and minute-long video generation, deployable on RTX 5090 GPUs. It uses a Linear DiT with linear attention (O(N) complexity) and a constant-memory KV cache for block linear attention, enabling long videos with fixed memory. Training cost is 12 days on 64 H100 GPUs (1% of…

Junsong Chen, Yuyang Zhao, Jincheng Yu, Ruihang Chu, et al.
Published
Sep 2025
Citations
82
Code
8.7K stars
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97

NVIDIA

LongLive: Real-time Interactive Long Video Generation

LONGLIVE is a frame-level autoregressive (AR) framework for real-time, interactive long video generation, addressing efficiency and quality challenges in diffusion and AR models. It introduces KV-recache to refresh cached states with new prompts for smooth, adherent prompt switches; streaming long tuning to enable train-long-test-long alignment; and…

Shuai Yang, Wei Huang, Ruihang Chu, Yicheng Xiao, et al.
Published
Sep 2025
Citations
189
Code
Not linked
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98

arXiv.org

OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models

OmniInsert is a unified framework for mask-free video insertion, allowing users to insert single or multiple reference subjects into a source video based on a text prompt. It addresses three key challenges: data scarcity, subject-scene equilibrium, and insertion harmonization. To tackle data scarcity, the authors propose InsertPipe, a data pipeline with…

Jinshu Chen, Xinghui Li, Xu Bai, Tianxiang Ma, et al.
Published
Sep 2025
Citations
9
Code
162 stars
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99

arXiv.org

OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

OmniWorld is a large-scale, multi-domain, multi-modal dataset for 4D world modeling, introduced by Shanghai AI Lab and ZJU. It comprises a self-collected OmniWorld-Game synthetic dataset (96K clips, 18.5M frames, 214+ hours) and curated public datasets from robot, human, and internet domains, totaling over 600K sequences and 300M frames. OmniWorld provides…

Yang Zhou, Yifan Wang, Jianjun Zhou, Wenzheng Chang, et al.
Published
Sep 2025
Citations
43
Code
489 stars
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100

arXiv.org

HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning

HuMo is a unified framework for Human-Centric Video Generation (HCVG) that enables collaborative control from text, reference images, and audio. It addresses two challenges: data scarcity and difficulty in coordinating sub-tasks of subject preservation and audio-visual sync. To overcome data scarcity, HuMo constructs a high-quality dataset with paired…

Liyang Chen, Tianxiang Ma, Jiawei Liu, Bingchuan Li, et al.
Published
Sep 2025
Citations
45
Code
1.3K stars
Read summary Original