Video Foundation Models

Follow this topic Ask about this topic

Latest papers

16 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation
Oct 4, 2026Team Kandinsky, Julia Agafonova, Bulat Akhmatov +85Audio-Video Generation Video Diffusion Models
CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding
Sep 30, 2026Yulong Liu, Xiaotian Han, Junyuan Shang +6Video Understanding Efficient VLM Inference
ERNIE Team, Baidu Inc. · The Hong Kong University of Science and Technology · Institute of Automation, Chinese Academy of Sciences (CASIA)
Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models
Sep 15, 2026Rwiddhi Chakraborty, Yinong, Wang +7Video-Language Model Evaluation Video QA
Oliver · Carnegie Mellon University · Texas A&M University +3
VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes
Sep 6, 2026Yan Ma, Jiadi Su, Zhulin Hu +4Vision Foundation Models Training Data Curation
Fudan University · Generative Artificial Intelligence Research Lab (GAIR) · Shanghai Jiao Tong University +2
What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models
Sep 1, 2026Sharon S. Musa, Fereshteh Forghani, Harrish Thasarathan +3Video Representation Learning Vision Foundation Models
Lassonde School of Engineering, York University, Toronto, Canada · Vector Institute, Toronto, Canada · FAIR, Meta Superintelligence Labs +2
Video Generation Models are General-Purpose Vision Learners
Jul 10, 2026Letian Wang, Chuhan Zhang, Rishabh Kabra +9Video Diffusion Models Vision Foundation Models
Google DeepMind · University of Toronto · University College London +3
Do Video Foundation Models Understand Intuitive Physics? A Layerwise Probing Analysis
Jun 8, 2026Samuele Punzo, Niccolò Caselli, Ippokratis Pantelidis +3Vision Foundation Models Video Representation Learning
University of Amsterdam, Netherlands.
PlayClass: Automated Play Behaviour Classification in Poultry
May 26, 2026Prince Ravi Leow, Neil Scheidwasser, Rebecca Oscarsson +3Video Action Recognition Video Foundation Models
Section for Health Data Science & AI, University of Copenhagen · 1Section for Health Data Science & AI, University of Copenhagen · Department of Infectious Disease Epidemiology, Imperial College London +2
Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models
May 18, 2026Svetlana Orlova, Niccolò Cavagnero, Gijs DubbelmanStreaming Video Understanding Vision Foundation Model Adaptation
Eindhoven University of Technology
PushupBench: Your VLM is not good at counting pushups
Apr 25, 2026Shengzhi Li, Jiarun Chen, Karun Sharma +2Multimodal Understanding Video-Language Model Evaluation
Motif-Video 2B: Technical Report
Apr 14, 2026Junghwan Lim, Wai Ting Cheung, Minsu Ha +25T2V Generation Video Foundation Models
Motif Technologies
THFM: A Unified Video Foundation Model for 4D Human Perception and Beyond
Mar 26, 2026Letian Wang, Andrei Zanfir, Eduard Gabriel Bazavan +2Video Foundation Models Vision Foundation Models
Google DeepMind
Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
Mar 23, 2026SII-GAIR, Sand. ai, : +43Multimodal Transformer Audio-Video Generation
Video Understanding: From Geometry and Semantics to Unified Models
Mar 18, 2026Zhaochong An, Zirui Li, Mingqiao Ye +9Video Foundation Models Video Understanding
Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark. · College of Computer Science, Nankai University, Tianjin 300350, China. · School of Computer and Communication Sciences, EPFL, Lausanne 1015, Switzerland. +7
Summer-22B: A Systematic Approach to Dataset Engineering and Training at Scale for Video Foundation Model
Feb 26, 2026Simo Ryu, Chunghwan HanVideo Foundation Models Vision Foundation Models
fal
UniVBench: Towards Unified Evaluation for Video Foundation Models
Feb 25, 2026Jianhui Wei, Xiaotian Zhang, Yichen Li +6Video Foundation Models Video Editing Benchmarks
Zhejiang University · ByteDance · Zhejiang Lab

Oct 4, 2026J/K move · Enter open · S save

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

Team Kandinsky, Julia Agafonova, Bulat Akhmatov, Mikhail Aksyutin+84

Audio-Video Generation Video Diffusion Models T2V Generation Audio-Visual Synchronization Video Foundation Models

Open paper AskPDF arXiv

We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, including lip-sync, in text-to-audio-video (T2AV) and image-to-audio-video (I2AV) modes; a built-in super-resolution model raises the output resolution to Full-HD (1920

\times

1080). Building on the video generation capabilities of Kandinsky 5.0, Kandinsky 6.0 Video employs a dual-stream CrossDiT architecture that connects a pretrained video stream and a newly trained audio stream through bidirectional cross-attention for temporal and semantic alignment. Our continuous pretraining strategy first trains the audio stream from scratch on large-scale audio corpora and then trains both streams jointly on paired audio-video data while preserving unimodal fidelity; pretraining is followed by supervised fine-tuning, reinforcement-learning-based post-training, and distillation. In side-by-side human evaluation, Kandinsky 6.0 Video Pro clearly outperforms its predecessor, Kandinsky 5.0 Video Pro, and remains competitive with leading audio-video generation models, particularly in speech quality. To accelerate open research and deployment in multimedia generation, we release the code, model checkpoints, and diffusers integration under the MIT license.