Vidu S2 - Shengshu Technology's real-time interactive and editable video model

What is Vidu S2

Vidu S2 is a real-timevideomodel launched by Shengshu Technology, open for experience upon release, featuring two core models. S2-Editing supports real-time style changes, outfit swaps, character swaps, and background swaps on video streams, with frame alignment technology ensuring no glitches during high-speed motion; S2-Avatar achieves real-time interaction at 720P, 25-42FPS, can insert reference images and swap outfits at any time, supports full-body motion tracking, and ranks best in nine StreamAV-Bench metrics. Its original SRF training puts an end to long-duration generation drift, and it introduces Spatial spatial video exploration for VR.

Vidu S2's main features

• S2-Editing real-time editing: Real-time style changes, virtual try-on, character swaps, and background swaps on a playing video stream, requiring only a text instruction plus an optional reference image.
• S2-Avatar real-time interaction: High-definition real-time character generation at 720P, 25-42FPS, supporting inserting new reference objects at any time and letting the character "catch" them while maintaining state memory.
• One-click real-time outfit swap: Without interrupting the video stream, upload an image and the character instantly completes an outfit swap, with continuous motion and no drop in emotion.
• Full-body motion tracking: Supports complete body performance for instructions such as solo dancing and 2D/3D animation, breaking through the limitations of traditional "talking head" digital humans.
• Vidu S2-Spatial spatial video: Converts monocular real-time video into synchronized left- and right-eye frames, providing spatialized real-time experiences for VR and other immersive devices.

Vidu S2's technical principles

• Frame-aligned sparse attention: The core underlying technology of S2-Editing, which during real-time editing strictly locks and aligns each frame of the new footage to the same moment in the original video, ensuring that motion trajectories after outfit or character swaps do not glitch and the image does not tear, solving the stability challenge of real-time image modification in high-speed motion video.
• SRF (Self-Replay Forcing) training technology: Originally created to put an end to the "collapse" and "drift" of long-duration streaming generation, making the model face its own generation history and self-correct errors, equivalent to giving the model a "self-correcting memory" so it can stably "keep performing" in continuously changing scenes.
• Dynamic reference mechanism: S2-Avatar allows users to add new reference images at any moment during video generation. The model responds instantly and integrates new elements into the frame while continuously retaining state information, achieving coherent interaction with causal relationships before and after.
• Spatialized video conversion: Through an algorithm, monocular real-time video is converted into synchronized left- and right-eye frames, simulating binocular parallax under a single-camera input, allowing the video to maintain temporal continuity while preserving consistent spatial perception, paving the way for real-time VR experiences.

How to use Vidu S2

• Web experience: Visit https://vidu.com/vidu-stream to try it out in real time.
• API integration: Developers can consult the documentation at platform.vidu.com/vidu-stream/doc and integrate S2 capabilities.
• Real-time editing: While a video stream is playing, enter a text instruction and upload an optional reference image to achieve style changes, outfit swaps, character swaps, and background swaps.
• Real-time interaction: Converse with S2-Avatar to drive character performance, and upload images at any time to have it display new items or perform a one-click outfit swap.

Vidu S2's core advantages

• Truly real-time: 720P, 25-42FPS streaming output; editing and interaction do not interrupt the video stream.
• Comprehensive editing capabilities: The four major real-time editing directions—style, clothing, character, and background—cover major commercial scenarios such as e-commerce, livestreaming, and marketing.
• Dynamic reference and state memory: New elements can be inserted at any time and the character can "remember" the context; the interaction is coherent and warm, breaking through the limitations of "talking head" digital humans.
• Strong long-duration stability: SRF self-correcting training puts an end to crashes and drift in streaming generation, allowing the character to perform stably for a long time.
• Leading benchmark results: S2-Avatar ranks best in nine StreamAV-Bench items, and Editing leads in key dimensions such as instruction execution, appearance restoration, and motion preservation.
• Low barrier, high return: Experience is open upon release; one original clip can be transformed into multiple stylized assets, significantly reducing set, shooting, and post-production costs.

Comparison of Vidu S2 with similar competitors

Application scenarios of Vidu S2

• E-commerce livestream selling: Fans request perfume or clothes, and the character takes them out for display in real time or changes outfits with one click, restoring a real shopping guide experience.
• Virtual try-on: Consumers can intuitively see how clothing fits body movements before purchase, reducing decision-making costs and return rates.
• IP commercialization: Brand virtual idols and game protagonists "parachute" into livestream rooms to sell goods on behalf of real people, maximizing fans' sense of immersion.
• Cross-border e-commerce/scenario marketing: One-click background replacement (selling jackets instantly switches to snowy mountains, selling beauty products places you at a Paris runway show), bringing set costs close to zero.
• Asset fission production: One live-action original clip generates multiple stylized assets in batches, such as cyberpunk, ink-wash, and anime styles, serving short-drama overseas expansion, game user acquisition, and major brand promotions.