# Kandinsky S2V 1.0: Transforming Speech into Engaging Video Animations

> Kandinsky S2V 1.0 is a newly developed family of models designed for converting speech into video animations. This system includes two versions: Kandinsky S2V 1.0 Lite, which contains 2 billion parame...

**Source**: arxiv.org | **Published**: 2026-10-15 | **Type**: research

## Key Facts

- Leverage Kandinsky S2V 1.0 to create engaging marketing videos from speech content.
- Implement advanced lip-sync technology to enhance user experience in video communications.
- Adopt the Pro version for high-quality animations in professional presentations and training materials.
- Integrate the system into customer service platforms for personalized video responses.
- Utilize the model’s multilingual capabilities to expand reach in diverse markets.

## Summary

**Paper:** [Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation](https://arxiv.org/abs/2610.19290)

**Authors:** Nikolay Semenov, Artem Riabinin, Sergey Bassenko, Anna Averchenkova, Julia Agafonova, Anastasia Aliaskina, Kirill Kozlov, Lev Novitskiy, Nikita Samsonov, Nikolai Gerasimenko, Denis Dimitrov

\## Executive Summary

Kandinsky S2V 1.0 is a newly developed family of models designed for converting speech into video animations. This system includes two versions: Kandinsky S2V 1.0 Lite, which contains 2 billion parameters, and Kandinsky S2V 1.0 Pro, which has 21 billion parameters. The models can generate 25 frames per second (FPS) videos that accurately animate lip movements, expressions, poses, and gestures based on a given speech waveform, a reference image, and a text prompt.

The technology is built on advanced architecture that integrates audio and text inputs via a model known as Qwen2.5-Omni. It also features a unique visual memory system that enhances the quality and responsiveness of the generated animations. The models were trained on a large dataset consisting of 1.17 million clips featuring single speakers, with captions in both English and Russian. This extensive training allows for a high level of detail and responsiveness to the audio input.

In comparative analyses, Kandinsky S2V 1.0 Pro demonstrated performance on par with or superior to existing open-source and proprietary commercial models, particularly excelling in lip synchronization. This means that when users generate video content using these models, the animated characters closely mimic the speech patterns and emotions conveyed in the audio, enhancing viewer engagement and realism.

The research also introduced distilled variants of both models, termed {\pi}-Flow, which optimize speed without sacrificing quality. Notably, the distilled version of Kandinsky S2V 1.0 Pro can generate video in just 16 sampling steps—significantly faster than the base model—while still achieving comparable or better results across various performance metrics.

Kandinsky S2V 1.0 ranks highly in common benchmarks for speech-to-video generation, indicating its robustness in this field. The models' performance has been validated through side-by-side evaluations that included both open-source and commercial offerings, reinforcing their competitiveness.

To support further research and application, the developers have made the code, model checkpoints, and integration tools available under an MIT license. This open-access approach is intended to foster innovation and enable other researchers and businesses to utilize and build upon this technology for their own projects.

In summary, Kandinsky S2V 1.0 represents a significant step forward in audio-driven animation technology, with potential applications in entertainment, education, and interactive media. The ability to generate high-quality animated videos from simple audio inputs could transform how content is created and consumed, offering new avenues for storytelling and information delivery.

\## Academic Abstract

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 Kandinsky S2V 1.0 Pro (21B parameters). From a single reference image, a speech waveform, and a text prompt, the models generate long-form 25 FPS video in which lip motion, expression, pose, and gesture follow the speech. The architecture draws text and audio conditioning from one Qwen2.5-Omni model and adds a reference-aware three-scale visual memory. Training starts from the Kandinsky 5.0 Video foundation models [1] and combines mixed reference-memory layouts with an area-adaptive face loss. The training corpus contains 1.17M single-speaker clips selected by active-speaker detection, with captions generated directly in English and Russian. A side-by-side human evaluation against the strongest open-source baseline and two proprietary commercial models shows that Kandinsky S2V 1.0 Pro performs comparably to or better than these models on average and is also preferred for lip synchronization. On common speech to video benchmarks, Kandinsky S2V 1.0 Lite and Kandinsky S2V 1.0 Pro rank among the top performing models according to mean rank over five reported metrics. Beyond the base models, we provide {\pi}-Flow distilled variants of both Kandinsky S2V 1.0 Lite and Kandinsky S2V 1.0 Pro. The distilled Kandinsky S2V 1.0 Pro model generates video in 16 sampling steps without classifier-free guidance, achieving a 5.4 times end-to-end speedup over the base model, while matching or exceeding it on nearly all reported benchmark metrics. To accelerate open research and deployment in speech-to-video generation, we release the code, model checkpoints, and diffusers integration under the MIT license.

## Frequently Asked Questions

**What business problems does Kandinsky S2V 1.0 solve?**

Kandinsky S2V 1.0 addresses challenges in generating realistic video content from audio input, allowing businesses to create engaging and dynamic visual presentations or animations that accurately reflect spoken content.

**Which industries could benefit most from Kandinsky S2V 1.0?**

Industries such as entertainment, education, marketing, and remote communication may benefit significantly from Kandinsky S2V 1.0, as it enables the creation of animated content that enhances storytelling and engagement.

**What are the practical implementation considerations for using Kandinsky S2V 1.0?**

Practical implementation considerations may include ensuring adequate computational resources to run the models, integrating the system with existing content creation workflows, and training staff to utilize the technology effectively.

**What resources or expertise are needed to implement Kandinsky S2V 1.0 in a business setting?**

Implementing Kandinsky S2V 1.0 may require access to high-performance computing infrastructure, expertise in AI model deployment, and familiarity with video production techniques to fully leverage its capabilities.

**What competitive advantages could businesses gain by using Kandinsky S2V 1.0?**

Businesses could gain a competitive advantage through enhanced content creation capabilities, allowing for more captivating and personalized visual experiences that may lead to increased audience engagement and improved communication of ideas.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/kandinsky-s2v-10-transforming-speech-into-engaging-video-animations)
- [Original source](https://arxiv.org/abs/2610.19290)
- [Kandinsky](https://welcome.ai/company/kandinsky): Featured company

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Source: Welcome.AI | https://welcome.ai/content/kandinsky-s2v-10-transforming-speech-into-engaging-video-animations