2 citations · 2 across the 4 of their papers we have counts for
6 papers
Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation
Nikolay Semenov, Artem Riabinin, Sergey Bassenko +8
We present Kandinsky S2V 1.0, an open family of speech-to-video models for controllable audio-driven human animation, comprising Kandinsky S2V 1.0 Lite (2B parameters) and Kandinsk…
Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation
Team Kandinsky, Julia Agafonova, Bulat Akhmatov +85
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 Kan…
Midpoint Generative Models
Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2
We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow Matching with linear interp…
Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko +22
This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis. The framework comprises three core lin…
VIVAT: Virtuous Improving VAE Training through Artifact Mitigation
Lev Novitskiy, Viacheslav Vasilev, Maria Kovaleva +2
Variational Autoencoders (VAEs) remain a cornerstone of generative computer vision, yet their training is often plagued by artifacts that degrade reconstruction and generation qual…
Unleashing the power of novel conditional generative approaches for new materials discovery
Lev Novitskiy, Vladimir Lazarev, Mikhail Tiutiulnikov +6
For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI ha…