T2V Generation

T2V: Text-to-Video

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Overview

Text-to-video generation studies how to turn language into coherent video, enabling uses such as storytelling, instruction, and creative production. Most systems build on diffusion transformers or flow-matching models, with prompt refinement, inference-time guidance, reference conditioning, and preference-based post-training used to improve control. Evaluation combines visual-quality and prompt-alignment measures such as VBench and T2V-CompBench with targeted tests of physics, temporal events, identity, and spatial relations. Models can produce convincing appearances, but reliable motion, physical interactions, and fine-grained prompt compliance remain difficult, while inference cost and safety add deployment challenges.

Latest papers

CardsList

110 papers14 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

  1. MotionCFG: Boosting Motion Dynamics via Stochastic Concept Perturbation

    Mar 14, 2026Byungjun Kim, Soobin Um, Jong Chul YeT2V GenerationVideo Diffusion Models

    Graduate School of AI, KAIST, Daejeon 34141, Republic of Korea · Department of AI, Kookmin University, Seoul 02707, Republic of Korea

  2. UniVid: Pyramid Diffusion Model for High Quality Video Generation

    Mar 14, 2026Xinyu Xiao, Binbin Yang, Tingtian Li +2Video Diffusion ModelsT2V Generation

  3. OSCBench: Benchmarking Object State Change in Text-to-Video Generation

    Mar 12, 2026Xianjing Han, Bin Zhu, Shiqi Hu +4T2V GenerationVideo Generation Evaluation

    National University of Singapore · Singapore Management University · Carnegie Mellon University +1

  4. Event-Driven Video Generation

    Mar 12, 2026Chika Maduabuchi, Jindong WangPhysical Consistency in Video GenerationVideo Diffusion Models

    William & Mary, Williamsburg, VA, USA

  5. Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreaking

    Mar 7, 2026Moyang Chen, Zonghao Ying, Wenzhuo Xu +4T2V GenerationAdversarial Text Attacks

    College of Science, Mathematics and Technology, Wenzhou-Kean University · AI Security Lab · State Key Laboratory of Complex & Critical Software Environment, Beihang University

  6. NEGATE: Constrained Semantic Guidance for Linguistic Negation in Text-to-Video Diffusion

    Mar 6, 2026Taewon Kang, Ming C. LinDiffusion Model GuidanceT2V Generation

    University of Maryland at College Park, United States

  7. Accelerating Text-to-Video Generation with Calibrated Sparse Attention

    Mar 5, 2026Shai Yehezkel, Shahar Yadin, Noam Elata +2Diffusion Model Inference AccelerationT2V Generation

    Apple · Tel Aviv University

  8. PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation

    Mar 3, 2026Shang Wu, Chenwei Xu, Zhuofan Xia +6Prompt Optimization for T2V GenerationPhysical Consistency in Video Generation

    Northwestern University · Dolby Laboratories

  9. BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation

    Mar 3, 2026Zihao Zhu, Ruotong Wang, Siwei Lyu +2T2V GenerationControllable Video Generation

    The Chinese University of Hong Kong, Shenzhen · State University of New York at Buffalo · Harbin Institute of Technology +1

  10. Does Semantic Noise Initialization Transfer from Images to Videos? A Paired Diagnostic Study

    Mar 3, 2026Yixiao Jing, Chaoyu Zhang, Zixuan Zhong +1Neural Network InitializationVideo Diffusion Models

    University of Michigan · University College London

  11. Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling

    Mar 2, 2026Zillur Rahman, Alex Sheng, Cristian MeoPrompt Optimization for T2V GenerationRetrieval-Augmented Video Generation

    Algoverse AI

  12. SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models

    Feb 27, 2026Yang Yang, Xinze Zou, Zehua Ma +2Diffusion Model WatermarkingRobust Watermarking

    the School of Electronic and Information Engineering, Anhui University, Hefei, Anhui 230601, China · Anhui Province Key Laboratory of Digital Security and the CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230026, China · the School of Computing, National University of Singapore, Singapore 117417

  13. SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation

    Feb 26, 2026Fengming Liu, Tat-Jen Cham, Chuanxia ZhengT2V GenerationDirect Preference Optimization

    College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.

  14. PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring

    Feb 23, 2026Injun Baek, Yearim Kim, Nojun KwakHuman-AI Co-CreationGenerative AI in Education

    Samsung Electronics, Suwon, Republic of Korea

  15. CHAI: CacHe Attention Inference for text2video

    Feb 18, 2026Joel Mathew Cherian, Ashutosh Muralidhara Bharadwaj, Vima Gupta +1T2V GenerationEfficient Video Generation

    Georgia Institute of Technology.

  16. Consistency-Preserving Diverse Video Generation

    Feb 17, 2026Xinshuang Liu, Runfa Blark Li, Truong NguyenVideo GenerationT2V Generation

    University of California, San Diego, CA, USA

  17. PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards

    Feb 2, 2026Minh-Quan Le, Gaurav Mittal, Cheng Zhao +3T2V GenerationVideo Generation

    1Microsoft · 2Stony Brook University · †Currently at Dolby Labs. 1Microsoft 2Stony Brook University

  18. Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding

    Jan 4, 2026Yixuan Lai, He Wang, Kun Zhou +1Video Diffusion ModelsT2V Generation

    State Key Lab of CAD&CG Zhejiang University Hangzhou, China · Department of Computer Science University College London London, UK

  19. PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation

    Dec 30, 2025Tianxin Xie, Wentao Lei, Kai Jiang +27Audio-Video GenerationVideo Generation Evaluation

    HKUST(GZ) · Tencent

  20. ContextAnyone: Context-Aware Diffusion for Character-Consistent Text-to-Video Generation

    Dec 8, 2025Ziyang Mai, Yu-Wing TaiVideo Diffusion ModelsDiffusion Transformer

    Dartmouth College