2 citations · 3 across the 10 of their papers we have counts for
12 papers
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…
Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer
Ivan Mikheev, Viacheslav Vasilev, Anna Dmitrienko +4
Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output. We show that…
Making Time Editable in Video Diffusion Transformers
Konstantin Kuklev, Viacheslav Vasilev, Alexander Kunitsyn +2
Modern Diffusion Transformers for video generation provide limited control over the progression of time and the editing of temporal dynamics. We propose a temporal-control methodol…
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…
Time-Correlated Video Bridge Matching
Viacheslav Vasilev, Arseny Ivanov, Nikita Gushchin +2
Bridge Matching (BM) learns a stochastic transport between two data distributions and is therefore a natural tool for data-to-data tasks such as video frame interpolation, image-to…
NABLA: Neighborhood Adaptive Block-Level Attention
Dmitrii Mikhailov, Aleksey Letunovskiy, Maria Kovaleva +6
Recent progress in transformer-based architectures has demonstrated remarkable success in video generation tasks. However, the quadratic complexity of full attention mechanisms rem…
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…
RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation
Viacheslav Vasilev, Julia Agafonova, Nikolai Gerasimenko +4
Text-to-image generation models have gained popularity among users around the world. However, many of these models exhibit a strong bias toward English-speaking cultures, ignoring…
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework
Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov +9
Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V)…
ESQA: Event Sequences Question Answering
Irina Abdullaeva, Andrei Filatov, Mikhail Orlov +6
Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as…
FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline
Vladimir Arkhipkin, Zein Shaheen, Viacheslav Vasilev +3
Multimedia generation approaches occupy a prominent place in artificial intelligence research. Text-to-image models achieved high-quality results over the last few years. However,…
Kandinsky 3.0 Technical Report
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6
We present Kandinsky 3.0, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progres…