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Evan: Welcome to Daily Paper Cast.

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Ashley: Today, we're diving into a paper from Hugging Face's daily paper list from September 15, 2026, which has received 121 upvotes.

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Evan: The title of the paper is 'Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation.'

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Ashley: This paper comes from Tsinghua University and Shengshu Technology, with lead authors Jintao Zhang and Kai Jiang, and the corresponding author is Zhijie Deng.

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Evan: Alright Ashley, let’s dive right into the introduction.

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What’s the main demand they're addressing with Vidu S2?

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Ashley: Recent video generation models, such as Sora and Veo, have shown impressive capabilities in generating high-quality videos.

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However, these models generally follow an offline, one-shot generation paradigm.

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Users have to input a prompt, wait for minutes or even tens of minutes, and receive the complete video only after the generation finishes.

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Evan: So, the user can’t interact with the video generation process in real-time?

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Ashley: Exactly.

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During this offline process, the user passively waits, unable to interact or adjust the video generation dynamically.

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This works for pre-generated content but falls short for interactive visual experiences like live streaming, face-to-face communication, and gaming.

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Evan: So how big is the demand for real-time interactive video generation compared to offline-generated content?

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Ashley: It's significantly higher.

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The authors use a hypothetical example: If each user has an average demand of around 50% for both real-time interactive and offline-generated content, the generation demand for real-time content would be much greater due to its unique interactive quality.

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Evan: Now, how does Vidu S2 aim to address this gap?

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Ashley: Vidu S2 is built on the foundation of its predecessor, Vidu S1, and it moves forward in two significant directions: Vidu S2-Avatar and Vidu S2-Editing.

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Vidu S2-Avatar enhances real-time interactive digital characters to support higher resolution, dynamic references, and stronger instruction following, even for complex actions like dancing.

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Evan: And what about Vidu S2-Editing?

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Ashley: Vidu S2-Editing can edit video streams in real-time, including style transfer, virtual try-on, character replacement, and background replacement.

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Essentially, it brings a new level of interactivity and editing flexibility to streaming video.

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Evan: That sounds like a big step forward.

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Did they include any new experiments or benchmarks?

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Ashley: Yes, they did.

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The authors have conducted experiments showing that Vidu S2 outperforms all existing baselines.

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Importantly, there's also an online demo available for users to experience these capabilities firsthand.

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Evan: Interesting.

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It sounds like they're going beyond just improving algorithms to making the whole user experience more dynamic and interactive.

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Ashley: Precisely.

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They are not only advancing the technical aspects but also ensuring that the technology can be experienced in practical, real-world scenarios, making it more accessible and user-friendly for interactive applications.

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Evan: Well, that wraps up our deep dive into the introduction of this paper.

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Ashley: Stay tuned as we get into the methodologies and experiments in a bit.

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Evan: Welcome back, everyone.

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Let's jump into the methods behind Vidu S2.

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Ashley: Alright, Evan.

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The methods are detailed and multifaceted, covering the Vidu S2-Avatar and Vidu S2-Editing components.

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Let's start with Vidu S2-Avatar, the real-time interactive digital-character model.

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Evan: Sounds good.

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How does Vidu S2-Avatar improve upon its predecessor, Vidu S1?

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Ashley: Vidu S2-Avatar makes four key advancements over Vidu S1.

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First, it introduces Self-Replay Forcing, a novel method to improve the quality and data efficiency by replaying the generated trajectory in a single gradient-enabled causal pass.

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This allows gradients to propagate across segments while avoiding the issue of history detachment.

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Evan: Interesting.

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And the second advancement?

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Ashley: The second improvement is the upgrade from 540p resolution to 720p, keeping the frame rate between 25 and 42 frames per second.

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A lightweight Refiner component is used to upscale the video in real-time, providing clearer and more detailed digital characters.

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Evan: What about the other two advancements?

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Ashley: Third, the instruction-following capability has been greatly enhanced.

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Vidu S2-Avatar can now follow a wider range of instructions, including complex actions like dancing.

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This is achieved by incorporating more diverse training data, including solo dance videos and 2D/3D animations.

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Ashley: Finally, dynamic reference interaction has been introduced.

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Users can now update reference images at any point during the video stream.

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For instance, the digital character can pick up a new object, change clothing, or switch backgrounds seamlessly.

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Evan: So, it sounds like Vidu S2-Avatar not only enhances video quality but also the interactive experience.

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How did they handle the data preparation for this?

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Ashley: Great question.

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The data preparation pipeline for Vidu S2-Avatar is comprehensive.

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Videos from diverse sources, such as talking heads, film, TV, solo dance, and animations, are processed through several stages: clipping, filtering, speech processing, captioning, and embedding.

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Additionally, they have enhanced operators for high-clarity selection and background stabilization to ensure superior training data quality.

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Evan: And how do they filter and select these high-clarity videos?

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Ashley: They use a multidimensional hybrid selection framework to evaluate and select videos not just based on resolution but also on frame rates, codec, pixel format, bit depth, and bitrate.

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This ensures that the selected videos are of the highest clarity suitable for 720p generation.

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Evan: That explains a lot.

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So, what about Vidu S2-Editing?

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Ashley: Vidu S2-Editing focuses on real-time video editing capabilities.

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It can perform style transfers, virtual try-ons, character replacements, and background replacements, all in real-time.

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Each of these functions utilizes frame-aligned attention to maintain the exact motion and timing of the original video, ensuring coherence and fluidity in the edited output.

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Evan: That sounds impressive.

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How do they prepare the data for these editing tasks?

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Ashley: For data preparation, they filtered videos through the Vidu S2-Avatar pipeline but applied stricter criteria to ensure high quality.

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They constructed four disjoint subsets for the different editing tasks, each comprising 200,000 videos.

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The goal was to cover a wide range of visual styles and content for training.

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Evan: And how do they handle style transfer specifically?

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Ashley: For style transfer, they first reconstruct a video from its surface-normal representation and a reference image using a model like NormalCrafter.

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They then replace the reference image with a stylized one to create a consistent stylized video that mimics the spatial structure and motion of the original.

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Evan: It seems like both components aim to offer robust real-time interaction and editing capabilities.

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Ashley: Exactly.

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Vidu S2 also explores the feasibility of real-time spatial video generation where the output can be experienced in virtual reality (VR).

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For instance, Vidu S2-Avatar can generate synchronized left- and right-eye views, enhancing immersive VR experiences.

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Evan: That's quite forward-thinking.

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How do they ensure low-latency during these complex processes?

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Ashley: They employ several optimization strategies like efficient attention mechanisms, kernel fusion, launch optimization, and multi-GPU parallelism.

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For instance, quantized linear-layer accelerations and custom CUDA kernels reduce memory usage and computational load, ensuring real-time, low-latency performance.

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Evan: Sounds like they've covered all bases to maintain performance without compromising on quality.

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Ashley: Precisely.

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The proposed methods in the paper are designed to ensure that real-time video generation and editing are not only feasible but also efficient and high quality, making interactive visual experiences more accessible.

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Evan: Well, that wraps up the Method section.

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Stay tuned as we dive into the experiments and results next.

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Evan: Welcome back!

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Let's delve into the experiments and the results they achieved with Vidu S2.

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Ashley: Sure, Evan.

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They evaluated Vidu S2 on two main tasks: streaming digital character generation with Vidu S2-Avatar and real-time video editing with Vidu S2-Editing.

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Evan: Alright, let's start with Vidu S2-Avatar.

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How did they set up the experiments for digital character generation?

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Ashley: For digital character generation, they used the StreamAV-Bench, a comprehensive benchmark suite that validates several aspects of streaming audio-video performance.

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They also ran an internal benchmark to compare Vidu S2-Avatar against commercial systems.

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Evan: What specific metrics did they measure?

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Ashley: The metrics included Visual Aesthetics, Visual Quality, Production Quality, Audio Quality, Audio–Visual Alignment, Audio–Visual Synchronization, Audio Instruction Fulfillment, Subject Consistency, and Background Consistency.

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Each metric evaluates different attributes such as visual appeal, motion naturalness, audio fidelity, and consistency over long durations.

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Evan: And how did Vidu S2-Avatar perform on these benchmarks?

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Ashley: Vidu S2-Avatar outperformed all other systems across every reported metric.

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For example, it scored highest in Visual Quality and Production Quality, indicating that its visual and audio output were top-notch.

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Notably, it also excelled in Audio–Visual Synchronization and Instruction Fulfillment, which are crucial for interactive applications.

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Evan: That’s quite comprehensive!

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How did they ensure the results were reliable?

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Ashley: They employed standardized protocols for the benchmarks and used aggregated results from multiple evaluators to ensure consistency.

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Every system received the same inputs and followed identical evaluation procedures to maintain fairness.

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Evan: What about the results from their internal benchmarks?

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Ashley: Internally, they conducted duration-stratified evaluations to see how Vidu S2-Avatar performs over time, from short clips of 10 seconds to longer streams of up to 90 seconds.

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Vidu S2-Avatar consistently maintained high ratings across all durations, particularly excelling in long-horizon stability and temporal consistency.

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Evan: Fascinating.

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Let’s now talk about the video editing task.

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How did they set up the experiments for Vidu S2-Editing?

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Ashley: For Vidu S2-Editing, they used a variety of public benchmarks including OpenVE-Bench, Sparkle-Bench, and RefVIE-Bench.

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These benchmarks evaluate instruction-guided video editing, reference-conditioned editing, and unpaired virtual try-ons.

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They also constructed an internal long-horizon test set.

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Evan: What metrics were used for evaluating video editing?

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Ashley: The metrics included Global Style for overall appearance, Background Change, Instruction Compliance, Visual Quality, Foreground Motion Preservation, and other specific criteria like Reference Fidelity and Temporal Consistency.

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Evan: And how did Vidu S2-Editing fare in these evaluations?

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Ashley: Vidu S2-Editing achieved the highest scores in almost all evaluated metrics.

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For instance, it scored 4.71 in Global Style and 4.14 in Background Change on OpenVE-Bench, surpassing even the strongest offline models like Bernini-R 14B.

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It also excelled in maintaining coherence and fluidity between edited and original video sequences.

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Evan: What about the results from the internal tests?

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Ashley: In the internal tests, Vidu S2-Editing was preferred by evaluators for its overall quality, video quality, temporal consistency, and semantic adherence.

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It outperformed other commercial systems like Decart-Lucy2.5 and XMax-X2.0.

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Evan: So, it seems Vidu S2-Editing was able to seamlessly integrate new elements while maintaining the integrity and quality of the original video.

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Ashley: Exactly.

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The experiments highlight Vidu S2’s capability to not only generate but also edit videos in real-time, all while maintaining high visual and audio fidelity.

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Evan: It’s impressive to see how comprehensive and robust their evaluation process was.

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Anything else notable from the experiments?

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Ashley: Yes, they also demonstrated Vidu S2's capability in generating spatial videos for VR headsets.

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They evaluated this by producing synchronized left- and right-eye views, enhancing the immersive experience without introducing noticeable latency.

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Evan: That's quite forward-thinking.

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It's clear they've thought through both the technical details and the practical applications.

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Ashley: The experiments validate that Vidu S2 is ready for real-world applications in areas like live streaming, virtual events, and interactive entertainment.

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Evan: Well, that wraps up the Experiment section.

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Stay with us as we continue to explore other aspects of this intriguing paper.

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Evan: Now, let's move onto the related work that Vidu S2 builds upon.

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Ashley, could you kick things off for us?

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Ashley: Of course, Evan.

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The related work section of the paper provides a comprehensive overview of the landscape of video generation and editing technologies.

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They discuss several key models and frameworks that paved the way for Vidu S2.

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Evan: What are some of the primary models they compare Vidu S2 to?

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Ashley: They start by mentioning recent video generation models like Sora, Veo, Wan, and Seedance.

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These models have shown strong capabilities in generating high-quality videos but typically follow an offline, one-shot paradigm.

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Evan: What are the limitations of these traditional models?

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Ashley: As I mentioned earlier, the offline generation paradigm requires users to input a prompt and wait for the entire video to be generated, which can take a significant amount of time.

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This process doesn't allow for real-time interaction or adjustments.

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Evan: Got it.

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So, how did the researchers aim to improve this with the Vidu series?

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Ashley: Vidu S1, the predecessor of Vidu S2, was one of the earliest attempts to provide real-time interactive video generation.

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It utilized TurboDiffusion and TurboServe to enable continuous user interaction, but had limitations in resolution and instruction-following capabilities.

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Evan: And how does Vidu S2 improve on those limitations?

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Ashley: Vidu S2 takes several steps forward by enhancing resolution to 720p, adding dynamic reference capabilities, and improving instruction adherence, even for complex actions like dancing.

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These advancements are built on the foundations laid by TurboDiffusion and TurboServe.

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Evan: That's interesting.

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What other models are mentioned in the related work?

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Ashley: The paper also references the Self-Forcing model, which addresses the train-test gap in autoregressive video diffusion.

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This model conditions each video segment on chunks it generated itself, although it still faced issues with efficiency and quality due to a lack of gradient flow through the self-generated history.

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Evan: How does Vidu S2 address these concerns?

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Ashley: Vidu S2 introduces Self-Replay Forcing, which re-noises and replays the student-generated trajectory in a single gradient-enabled pass.

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This ensures that gradients can flow across segments, improving data efficiency and training quality.

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Evan: What about the real-time video editing capabilities?

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Are there any notable related works there?

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Ashley: Yes, the paper discusses various existing video editing models like Bernini, SCAIL-2, and CoinVE-Edit.

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These models offer robust editing capabilities but often operate offline and can't handle the real-time demands that Vidu S2-Editing addresses.

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Evan: So, what makes Vidu S2-Editing stand out in the landscape of video editing models?

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Ashley: Vidu S2-Editing distinguishes itself by enabling real-time edits with frame-aligned attention.

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This ensures that the edits maintain the same motion and timing as the original video, making the result appear seamless and natural.

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Evan: It sounds like these contributions are addressing some significant gaps in the field.

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Is there anything else notable in the related work about how Vidu S2 compares to competitors?

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Ashley: Indeed, the paper also explores real-time spatial video generation and editing aimed at enhancing VR experiences.

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This is something not extensively covered in earlier models and shows Vidu S2's forward-thinking approach.

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Evan: It's fascinating to see how these models evolved and where Vidu S2 fits within the broader context of video generation and editing technologies.

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Ashley: The related work effectively highlights the advancements made by Vidu S2 while acknowledging the foundational work of previous models in the field.

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Evan: Alright, that brings us to the end of the related work section.

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Stay with us as we delve into more details of this fascinating paper.

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Evan: Welcome back!

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Let's summarize the key contributions and takeaways from the Vidu S2 paper.

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Ashley: To recap, Vidu S2, which includes Vidu S2-Avatar and Vidu S2-Editing, makes significant advancements in real-time interactive video generation and editing.

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Evan: For Vidu S2-Avatar, the improvements focus on enhancing video quality to 720p, supporting dynamic references, and improving the instruction-following capabilities for complex actions like dancing.

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Ashley: And for Vidu S2-Editing, the emphasis is on real-time video editing capabilities.

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This includes style transfer, virtual try-on, character replacement, and background replacement, all performed seamlessly with frame-aligned attention to maintain coherence and fluidity.

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Evan: The data preparation and optimization methods are thorough, ensuring high clarity and stability of videos.

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They used diverse video sources and sophisticated selection frameworks to gather the best training data.

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Ashley: Their experiments demonstrated that Vidu S2 surpasses existing baselines in all evaluated metrics for both digital character generation and video editing.

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Notably, they showed impressive results in visual quality, motion naturalness, and temporal consistency.

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Evan: Exactly.

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The work also explores the potential for real-time spatial video generation, making significant strides towards enhancing immersive VR experiences.

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Ashley: Overall, Vidu S2 is not just an incremental improvement.

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It represents a significant leap in both the functionality and usability of real-time video generation and editing technologies, making interactive visual experiences more accessible and practical.

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Evan: That’s all for today's discussion on 'Daily Paper Cast.' Thanks for tuning in!

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Ashley: We hope you enjoyed our deep dive into Vidu S2.

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Make sure to join us in future episodes as we continue to explore the latest research in AI, NLP, and more.

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Evan: Until next time, keep questioning, keep learning, and stay curious.

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Goodbye!

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Ashley: Goodbye, everyone!