Title: Audio-Sync Video Generation with Multi-Stream Temporal Control
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video generation
Abstract: Audio is inherently temporal and closely synchronized with the visual world, making it a naturally aligned and expressive control signal for controllable video generation (e.g., movies).
Beyond control, directly translating audio into video is essential for understanding and visualizing rich audio narratives (e.g., Podcasts or historical recordings).
However, existing approaches fall short in generating high-quality videos with precise audio-visual synchronization, especially across diverse and complex audio types.
In this work, we introduce MTV, a versatile framework for audio-sync video generation. MTV explicitly separates audios into speech, effects, and music tracks, enabling disentangled control over lip motion, event timing, and visual mood, respectively—resulting in fine-grained and semantically aligned video generation.
To support the framework, we additionally present DEMIX, a dataset comprising high-quality cinematic videos and demixed audio tracks. DEMIX is structured into five overlapped subsets, enabling scalable multi-stage training for diverse generation scenarios.
Extensive experiments demonstrate that MTV achieves state-of-the-art performance across six standard metrics spanning video quality, text-video consistency, and audio-video alignment.

Audio-Sync Video Generation with Multi-Stream Temporal Control

Abstract

Audio is inherently temporal and closely synchronized with the visual world, making it a naturally aligned and expressive control signal for controllable video generation ( e.g. , movies). Beyond control, directly translating audio into video is essential for understanding and visualizing rich audio narratives ( e.g. , Podcasts or historical recordings). However, existing approaches fall short in generating high-quality videos with precise audio-visual synchronization, especially across diverse and complex audio types. In this work, we introduce MTV, a versatile framework for audio-sync video generation. MTV explicitly separates audios into speech, effects, and music tracks, enabling disentangled control over lip motion, event timing, and visual mood, respectively-resulting in fine-grained and semantically aligned video generation. To support the framework, we additionally present DEMIX, a dataset comprising high-quality cinematic videos and demixed audio tracks. DEMIX is structured into five overlapped subsets, enabling scalable multi-stage training for diverse generation scenarios. Extensive experiments demonstrate that MTV achieves state-of-the-art performance across six standard metrics spanning video quality, text-video consistency, and audio-video alignment. Project page: https://hjzheng.net/projects/MTV/ .

1 Introduction

Audio is a fundamental medium in daily life, crucial for both information delivery ( e.g. , communication, notifications, and education) and immersive experiences ( e.g. , enhancing the impact of film visuals). Despite the prevalence of audio-centric platforms ( e.g. , Podcasts), content presented solely through audio lacks the visual dimension needed to fully convey the richness of events. Since audio is naturally temporal and inherently synchronized with the visual world, researchers [1-3] have devoted considerable attention to translating audios into corresponding videos to enhance audience understanding of rich audio narratives ( e.g. , historical recordings).

Despite great progress, existing methods face practical limitations in generating high-fidelity cinematic videos with precise synchronization ( e.g. , pouring water into the transparent cup), primarily due to: (i) Under-specified audio-visual mapping. Current approaches handle a wide spectrum of audios and map them to various target scenes ( e.g. , landscapes [4], dancing [5], music performances [6]). This broad representation scope potentially leads to ambiguous mappings lacking specificity between audio and visual features. (ii) Inaccurate temporal alignment. Existing methods primarily focus on

… A man in a brown jacket and a blue shirt … talking on a mobile phone …

Character-centric narrative

... A male on the left, wearing a

black

suit ... A woman on the right, wearing a red dress ...

Multi-character interaction

… The focus is on the water's movement as it is poured into the glass from above …

Sound-triggered events

… A young woman … flowers with purple and yellow … A man dressed in a dark jacket ...

Music-shaped ambiance

… An old, rusted car driving on a suburban street ... with a faded white paint job …

Camera movement

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building scene-level semantic consistency ( e.g. , translating engine sound to a car-centered video), struggling with accurate timing correspondence between individual audio events and their visual features ( e.g. , speech [7], motion [8], and visual mood [9]).

In this paper, we propose the MTV framework, enabling M ulti-stream T emporal control for audiosync V ideo generation to overcome aforementioned issues, with versatile capabilities across scenarios illustrated in Fig. 1. Instead of attempting a direct mapping from composite audios, we explicitly separate audios into distinct controlling tracks ( i.e. , speech, effects, and music), inspired by CDX'23 1 . To provide sufficient high-quality video clips with demixed audio tracks, we contribute a large-scale DEMIX dataset with tailored data processing, including 392K video clips with 1.2K hours. These tracks enable the model to precisely control lip motion, event timing, and visual mood, resolving the ambiguous mapping. To further incorporate rich visual semantics beyond direct audio cues, we leverage features ( e.g. , subject gesture, scene appearance, camera movement) initially derived from a pretrained text-to-video model [10], and subsequently finetuned using video clips from the DEMIX dataset. To enable the progressive extension of learned high-level video semantic features stage-by-stage, this dataset is structured into five overlapped subsets. A multi-stage training strategy is introduced to learn concrete and localized controls ( e.g. , lip motion) towards more abstract and global influences ( e.g. , visual mood), leading to clear audio-visual relationships.

To achieve accurate temporal alignment, we propose the Multi-Stream Temporal ControlNet (MSTControlNet) within the MTV framework. The interval stream is designed for specific feature synchronization, which extracts features from the speech and effects tracks. It employs interval interaction blocks to understand each track individually and construct their interplay, maintaining the coherence with inferred semantic features. After that, interval feature injection module inserts features of each track into corresponding time intervals to drive lip motion and event timing. Since visual mood typically covers the entire video clip, the holistic stream is designed for overall aesthetic presentation, which extracts features from the music track using the holistic context encoder. These features then serve as style embeddings, applied uniformly to all frames through global style injection, controlling the visual mood.

We summarize our contributions as follows:

We present MTV, a versatile audio-sync video generation framework by demixing audio inputs, achieving precise audio-visual mapping and accurate temporal alignment.

We introduce an audio-sync video generation dataset structured into five overlapped subsets, presenting the multi-stage training strategy for learning audio-visual relationships.

We propose the multi-stream temporal ControlNet to distinctively process demixed audio tracks and precisely control lip motion, event timing, and visual mood, respectively.

2 Related Works

2.1 Video Diffusion Model

The field of video generation has made significant progress with the adoption of diffusion models. Early approaches [11-13] extend the dynamic modeling capabilities of pretrained text-to-image diffusion models [14] by incorporating temporal layers ( e.g. , 3D convolutions [15] and temporal attention [16]). However, these methods face inherent challenges in capturing long-range spatialtemporal dependencies due to the convolutional architectures of their backbone ( e.g. , UNet [17]). To overcome this limitation, Sora report [18] presents the potential of the diffusion transformer (DiT) [19] architecture, prompting a shift towards integrating 3D VAE [20] for spatial-temporal compression and scaling up to train the entire DiT-based model. Further improvement has been achieved by recent foundation models through adaptive layernorm modules [10], progressive scaling [21, 22], and post-training techniques [23]. These advancements in text-to-video models provide a strong foundation and powerful generative priors that could potentially be leveraged for related cross-modal tasks, such as high-quality audio-sync video generation.

2.2 Audio-driven Image Animation

Audio-driven image animation aims to generate dynamic visuals from a static image, synchronized with user-provided audios. Several previous works animate general objects or scenes while maintaining audio-visual consistency. Sound2Sight [24] and CCVS [25] leverage the context of preceding frames to achieve audio-driven subsequent frames generation. TPOS [26] uses audios with variable temporal semantics and amplitude to guide the denoising process. ASV A [27] incorporates a temporal audio control module for effective audio synchronization. Other works concentrate on audio-driven human animation. Talking head [7, 28-30] focus on animating human face images to produce lip motion that synchronize with the speech. Recent works extend animation beyond the head to include half-body movements [31] and introduce pose control for full-body animation [32]. Another specific application is music-to-dance [33, 34], which generates human dance according to the beat of the music. Despite the audio-visual synchronization of these methods, their reliance on static images restricts models' capability to generate dynamic scenes required for cinematic videos.

2.3 Audio-sync Video Generation

Audio-sync video generation does not require additional images for reference, offering the potential for free scene creation. Early works are designed based on VQGAN [35] and StyleGAN [36], achieving audio control through multi-modal autoregressive transformers [2] and style code alignment [4, 37]. Recently, following the success of diffusion models demonstrating effectiveness in general video generation, researchers have turned their attention. Highlighting the benefit of multi-modal

conditions, TA2V [6] demonstrates that conditioning on both text descriptions and audio inputs significantly enhances the quality of generated videos. To achieve audio-visual alignment at both global and temporal levels, TempoTokens [1] designs a lightweight adapter for text-to-video generation model. Introducing a unified diffusion architecture, MM-Diffusion [5] enables both joint audio-video generation and zero-shot audio-sync video generation. Leveraging diffusion-based latent aligners for open-domain audio-visual generation, Xing et al. [3] achieve the audio-sync video editing and opendomain content creation. Although great progress has been made, audio-sync video generation still faces under-specific audio-visual mapping and inaccurate temporal alignment. Therefore, achieving cinematic quality remains challenging.

3 Dataset

We introduce the DEMIX dataset, tailored for training demixed audio-sync video generation models.

Data source. The training data is sourced from three aspects: (i) 65 hours of talking head videos from CelebV-HQ [45]; (ii) 4,923 hours of cinematic videos from MovieBench [46] (69h), Condensed Movies [47] (1,270h), and Short-Films 20K [48] (3,584h); and (iii) 8,903 hours film-related videos from YouTube. All collected videos include their accompanying audio tracks.

Video filtering. Following previous video generation models [10, 12, 49], we use PySceneDetect [50] to segment video into single-shot clips. Audiobox-aesthetics [51] is further used to assess the quality of accompanying audio, removing clips with low scores. For the left video clips, we annotate each one with text descriptions using LLaVA-Video [52].

Demixing filtering. To improve audio demixing reliability, we employ a dual-demixing comparison strategy, comparing demixing outputs from MVSEP [53] (speech, effects, music) and Spleeter [54] (speech, others). After that, we calculate the L1 distance between the speech tracks. Next, the 'others' track from Spleeter is conditionally compared: to the effects track from MVSEP if music is silent (below -45dB), and to the music track if effects are silent. Clips are discarded only if high L1 distances are found on any of the comparable pairs.

Voice-over filtering. To build clear audio-visual relationships for cinematic videos, we first detect whether people are present in the videos using YOLO [55]. Next, we perform speaker diarization for the accompanying audio using Scribe [56] to identify active speaker segments and count the number of speakers. After that, we detect the active speaker from videos for each frame using TalkNet [57]. As a result, we can discard clips where speech occurs in the audio but the video analysis detects neither a visible person nor an active speaker in the corresponding frames.

Subset division. To facilitate multi-stage training for versatile audio-sync video generation models, the filtered DEMIX data is structured into five overlapped subsets. The basic face subset comprises all talking head videos. The remaining cinematic and film-related videos are then categorized to form the other subsets: assignment to single character or multiple characters depends on the annotated human count, while assignment to sound event or visual mood occurs if the respective effects or music track is non-silent.

Data statistics. After data collection and filtering, our DEMIX dataset includes 18K basic face, 54K single character, 39K multiple characters, 166K sound event, and 195K visual mood data, tailored for

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3D full-attention

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Person2:

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the right with graying hair,

wearing a casual plaid shirt.

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cinematic videos, totaling non-overlapped 392K clips with 1.2K hours, accompanied by demixed audio tracks 2 . For comprehensive evaluation, we hold out 1K video clips from the dataset to form the testing set. We provide an additional comparison with existing audio-related datasets [4, 38-44] in Tab. 1, highlighting that ours is tailored for versatile audio-sync video generation using demixed audio tracks, while robustly covering scenarios with people, objects, and cinematic visuals.

4 Method

This section begins with an overview of our MTV framework for audio-sync video generation (Sec. 4.1). Next, we detail the Multi-stream Temporal ControlNet (MST-ControlNet), including the interval stream for specific feature synchronization, and the holistic stream for overall aesthetic presentation (Sec. 4.2). Finally, we present the multi-stage training strategy for effectively learning audio-visual relationships (Sec. 4.3).

4.1 Overview

MTV generates audio-sync videos based on user-provided text descriptions y (specifying the scenes and subjects) and demixed audio tracks a = { a s , a e , a m } (representing speech, effects, and music) to respectively drive the lip motion, event timing, and visual mood. The pipeline is illustrated in Fig. 2.

Video compression. As presented in Fig. 2 (a), MTV is equipped with a pretrained spatio-temporal variational autoencoder (V AE) encoder E to map video clips x into latent code z 0 = E ( x ) . After that, its corresponding VAE decoder D is used to reconstruct video clips from the latent code x = D ( z 0 ) .

Denoising network. As presented in Fig. 2 (b), we concatenate the text embeddings f y and noised latent code z t before feeding them into the network to ensure the video-text correspondence. The expert Adaptive LayerNorm (AdaLN) [10] then independently processes text and video features within this unified sequence. Next, 3D full-attention is used to interact semantics of text embeddings with corresponding video features. After being extracted by MST-ControlNet, audio cues are integrated via the interval feature injection and holistic style injection mechanisms. Finally, a feed-forward network (FFN) is used to refine the resulting video features.

Denoising process. As presented in Fig. 2 (c), MTV finally generates audio-sync videos by iteratively denoising latent codes. During training, at each time step t ∈ { 0 , . . . , T } , Gaussian noise ϵ t ∼

N (0 , 1) is added to the clean latent code z 0 to produce a noised latent code z t = √ ¯ α t z 0 + √ 1 -¯ α t ϵ t . A diffusion transformer ϵ θ is trained to predict the noise ϵ t , given the noised latent code z t , demixed audio tracks a , denoising time step t , and text descriptions y . The diffusion transformer is trained by minimizing the loss:

For inference, we iteratively denoise a randomly sampled noise z T ∼ N (0 , 1) to obtain the latent code z ′ 0 to generate video clips with the V AE decoder x ′ = D ( z ′ 0 ) .

4.2 Multi-stream Temporal ControlNet

After explicitly separating audios into speech, effects, and music tracks, we propose the MSTControlNet to achieve accurate temporal alignment by respectively controlling lip motion, event timing, and visual mood. As presented in Fig. 2 (d), the architecture consists of an audio encoding module followed by two specialized streams.

Audio encoding. Given demixed audio tracks a = { a s , a e , a m } , we initially extract their corresponding features { f s , f e , f m } from the demixed tracks using wav2vec [58]. After that, speech and effect features are fed into the interval stream for specific feature synchronization. Instead, music features are fed into the holistic stream for overall aesthetic presentation.

Interval stream. We design the interval stream to interval-wise control the lip motion and event timing. Specifically, we separately process speech features f s and effect features f e with a stack of linear layers and concatenate them before feeding them into N interval interaction blocks. Within each block, these features are processed independently (via AdaLN, Gate, and FFN) to refine per-track understanding. To model their interplay at each time interval i , the corresponding speech features f s i and effects features f e i are jointly processed by a self-attention [ ˜ f s i , ˜ f e i ] = SelfAttn ([ f s i , f e i ]) . This interaction also maintains the coherence with inferred semantic features. Finally, interacted speech features ˜ f s and effects features ˜ f e are integrated into their corresponding time intervals via the interval feature injection mechanism:

where h i represents the video latent code at i -th interval. CrossAttn( · , · ) means a cross-attention, where the latent code serves as the query and the audio features as the key and value. Let M be the number of intervals, the resulting latent code is then updated as h ′ = { h s i + h e i } i M =1 .

Holistic stream. The holistic stream is designed to control the visual mood for the entire video clip. Specifically, we process the music features f m through a holistic context encoder, comprising three linear layers and a 1D convolutional layer to extract features representing the visual mood. Since the environmental ambiance typically covers the entire video clip, an average pooling is applied to merge all the intervals and transform them into holistic music features ˜ f m . Next, these features are regarded as style embeddings. By independently transforming these features into scale factor γ m = Linear ( ˜ f m ) and shift factor β m = Linear ( ˜ f m ) , we modulate the video latent code h ′ uniformly across all intervals via the holistic style injection:

where h m is the modulated latent code, fed into the denoising network to refine video features.

4.3 Multi-stage training strategy

As the dataset is structured as five overlapped subsets, we introduce the multi-stage training strategy to progressively scale up the model stage-by-stage.

Text structure. As presented in Fig. 2 (e), we create a template to structure text descriptions, enabling our MTV framework to be compatible with these distinct training subsets. Specifically, this template begins with a sentence indicating the number of participants ( e.g. , 'Two person conversation'), based on Scribe [56] speaker counts. It then consists of subsequent entries for each individual, starting with a unique identifier ( e.g. , Person1 , Person2 ) followed by their respective appearance description. Following these individual entries, an explicit identifier for the currently active speaker is specified. Finally, a sentence provides an overall description of the scene. Notably, when there is no active speaker in the video, only the overall description will be provided.

Training schedule. We train the model from concrete and localized controls towards more abstract and global influences. Initially, we train the model to learn lip motion using the basic face subset. It then learns human pose, scene appearance, and camera movement on the single character subset. To handle scenarios with multiple speakers, we subsequently train the model on the multiple characters subset. Following this, our training focus shifts to event timing and extending subject understanding from humans to objects using the sound event subset. Finally, we train the model on the environmental ambiance subset to improve its representation of visual mood.

Training details. We initialize our spatial-temporal V AE and DiT backbone with pretrained weights from CogVideoX [10] and train our model to generate audio-sync videos at a 480 × 720 resolution. For each stage, we train our model for 40K steps on 24 NVIDIA A800 GPUs using the Adam-based optimizer [59] with a learning rate of 1 × 10 -5 , where MST-ControlNet and attention layers of the backbone are trainable. For inference, our model requires 280s to generate a 49-frame audio-sync video on a NVIDIA A100 GPU.

5 Experiments

5.1 Comparison with state-of-the-art methods

As audio-sync video generation is an emerging task, the relevant comparison methods are still developing. We compare our method with three recent state-of-the-art approaches in our DEMIX dataset. For TempoTokens [1] and Xing et al. [3], we evaluate them using both text descriptions and corresponding audios as their original configuration. Since MM-Diffusion [5] can only support audio inputs and its training focuses on specific landscape and dancing, we finetune it to ensure a fair comparison. 50 videos are randomly selected from the testing set for evaluation.

Quantitative comparisons. As presented in Tab. 2, we quantitatively evaluate performance across three main aspects: (i) Visual quality is assessed using Frechét Video Distance (FVD) [60]. (ii) Temporal consistency (Temp-C) is measured by calculating similarity between consecutive frames using CLIP [61]. (iii) We examine text-video alignment via Text Consistency (Text-C) [62], audiovideo alignment using Audio Consistency (Audio-C) [63], and specifically lip motion synchronization with Sync-C and Sync-D [64]. As a result, our framework outperforms state-of-the-art methods across all six quantitative metrics. These metric details are provided in the supplementary materials.

Qualitative comparisons. As presented in Fig. 3, qualitative comparisons with state-of-the-art methods [1, 3, 5] highlight the advantages of our framework. For instance, even after finetuning MM-Diffusion [5] for over 320K steps using the official code on 8 NVIDIA A100 GPUs, it still struggles with generating cinematic videos. TempoTokens [1] struggles to generate cinematic videos for complex text-specified scenarios, resulting in unrealistic human expressions (Fig. 3 left). Xing et al. [3] find it difficult to effectively achieve audio synchronization for specific event timing, leading to incorrect rendering of human gestures for guitar performance (Fig. 3 right). In contrast, our MTV framework faithfully generates audio-sync videos with cinematic quality.

MM-Diffusion

… The man has gray hair … The woman is wearing a denim shirt …

TempoTokens

Ours

… A person … playing the guitar … red and purple stage lights …

Ours

W/o

TB

W/o

SI

W/o

SE

… An older man … wearing a faded blue coat … photograph ….

… A woman with dark skin … a long-sleeved, v-necked dress …

RAGAA

ANCAGR

AADHA

5.2 Ablation Study

To evaluate the effectiveness of key components within MST-ControlNet, we conduct ablation studies against three baseline configurations, as shown in Fig. 4 and Tab. 2.

W/o SE (Separate Extraction). We extract all features from demixed audio tracks using interval interaction blocks. This prevents music features from shaping the overall aesthetic presentation, leading to reduced visual mood (Fig. 4 left, degraded FVD and Temp-C).

W/o SI (Separate Injection). We extract features from demixed audio tracks by their respective encoders. These features are then concatenated and injected into the denoising network via a shared cross-attention. This reduces conditional consistency (Fig. 4 left, decreased Text-C and Audio-C).

W/o TB (Training Backbone). We freeze all weights of DiT backbone and only train our proposed MST-ControlNet to preserve more generative priors. This impairs the specific feature synchronization, especially the lip motion synchronization (Fig. 4 right, reduced Sync-C and Sync-D).

5.3 User Study

To better evaluate our method from a human perception perspective, we conduct three subjective user study experiments in Tab. 3. We present videos generated by our method and all baselines to participants and ask them to choose the best one based on the following criteria: (i) Semantic consistency. How well the video content aligns with the text description. (ii) Motion fluency. The realism and temporal coherence of the motion. (iii) Overall preference. How good the holistic quality of the video is. For each study, we randomly select 50 text descriptions from the test set, and the evaluations are conducted by 25 volunteers. The table below shows the percentage of times each method is chosen as the winner. Our method is consistently favored by human observers and has achieved the highest scores across all three subjective criteria.

5.4 Analysis of Pre-trained Components

We evaluate the robustness of our proposed method by integrating it with alternative pre-trained components. Specifically, we test replacing the audio encoder (Wav2Vec/BEATs) and the video backbone (CogVideoX/Wan14B) in Tab. 4.

BEATs. Since Wav2Vec [58] is a common setting for speech encoding ( e.g. , Hallo3 [7]), this baseline only replaces it with BEATs [65] for both the effects and music tracks. As shown in Tab. 4, this baseline achieves comparable (or slightly better) video-related metrics ( i.e. , FVD and Temp-C) but shows a slight degradation on audio-related metrics ( i.e. , Audio-C, Sync-C, and Sync-D), suggesting that our current choice of Wav2Vec [58] is a robust and effective one for this task.

Wan14B. Since Wan14B [21] shares a similar DiT-based structure with CogVideoX [10], we can integrate our proposed MST-ControlNet into it without architectural changes. Specifically, our interval feature injection and holistic style injection modules are added after each text cross-attention layer. The quantitative results below show this baseline achieves better performance on videoand text-related metrics ( i.e. , FVD, Temp-C, and Text-C) due to the stronger capabilities of the Wan14B [21], while achieving comparable performance on all audio-related metrics ( i.e. , Audio-C, Sync-C, and Sync-D).

5.5 Application

As presented in Fig. 5, our model support four typical scenarios: (i) By integrating text-to-video generative priors and learned audio-visual synchronized capabilities, our model can create vivid virtual characters. (ii) Given user-provided images and taking them as arbitrary keyframes, our model can drive the image according to the given audios. (iii) Although our model generates video segments of 49 frames, it can achieve long video generation by using the generated frame to initialize the next segment. (iv) Following training-free approaches [66], our model can generate scene transitions guided by providing time-varying text descriptions.

Scene transitions

… A dark knight rides the majestic black horse at a steady gallop across a

vast grassland

/

snow-covered field

…

Long video generation

… A woman with shoulder-length dark hair, wearing a light-colored blazer over a dark top … a vase with flowers …

Character creation

… Wolf wearing sunglasses and owl wearing suit …

Keyframe guidance

… A woman … a red dress with white fur …

Appearance

Left hand pulling

Changing hands

Sad

Peaceful

Event timing

Lip motion

Visual mood

Right hand pressing

Red top, shor hair

Black top, middle hair

Gray top, long hair

/ou/

/si:/

/a:/

Happy

5.6 Controllability

As shown in Fig. 6, leveraging control from both text descriptions and the three demixed audio tracks ( i.e. , speech, effects, music), our model can offer controllability across following four key aspects: (i) Modifying the text descriptions while keeping all audio tracks fixed allows the visual scene appearance to be edited without affecting the audio synchronization. (ii) Given a demixed speech track, the model enables precise control over the synchronized lip motion of the generated character. (iii) Similarly, with a demixed effects track, the model accurately synchronizes event timing with the sound effects. (iv) By changing the demixed music track, the model creates different visual moods for the generated video.

6 Conclusion

In this work, we presented MTV, a versatile framework for audio-sync video generation. MTV leverages generative priors from pretrained text-to-video models [10] and is trained on our contributed DEMIX dataset that provides sufficient cinematic videos with demixed audio tracks. Equipped with our proposed MST-ControlNet, MTV is able to independently control lip motion, event timing, and visual mood. Combined with a multi-stage training strategy for effective learning of complex audio-visual relationships, MTV achieves state-of-the-art performance across six evaluation metrics.

Limitation. Although our approach demonstrates the potential of using demixed audio tracks for precise video control, it is fundamentally limited by the scope of categories provided by upstream audio demixing techniques [53, 54]. We believe the capabilities of audio-sync video generation methods will further progress with advancements in audio demixing methods.

Acknowledgement. This work is supported by National Natural Science Foundation of China (Grant No. 62136001). We thank all the insightful reviewers for the helpful suggestions, and the colleagues at Beijing Academy of Artificial Intelligence for their support throughout this project.

References

G. Yariv, I. Gat, S. Benaim, L. Wolf, I. Schwartz, and Y. Adi, 'Diverse and aligned audio-tovideo generation via text-to-video model adaptation,' in AAAI , 2024.

S. Ge, T. Hayes, H. Yang, X. Yin, G. Pang, D. Jacobs, J.-B. Huang, and D. Parikh, 'Long video generation with time-agnostic vqgan and time-sensitive transformer,' in ECCV , 2022.

Y. Xing, Y. He, Z. Tian, X. Wang, and Q. Chen, 'Seeing and hearing: Open-domain visual-audio generation with diffusion latent aligners,' in CVPR , 2024.

S. H. Lee, G. Oh, W. Byeon, J. Bae, C. Kim, W. J. Ryoo, S. H. Yoon, J. Kim, and S. Kim, 'Sound-guided semantic video generation,' in ECCV , 2022.

L. Ruan, Y. Ma, H. Yang, H. He, B. Liu, J. Fu, N. J. Yuan, Q. Jin, and B. Guo, 'MM-Diffusion: Learning multi-modal diffusion models for joint audio and video generation,' in CVPR , 2023.

M. Zhao, W. Wang, T. Chen, R. Zhang, and R. Li, 'TA2V: Text-audio guided video generation,' TMM , 2024.

J. Cui, H. Li, Y. Zhan, H. Shang, K. Cheng, Y. Ma, S. Mu, H. Zhou, J. Wang, and S. Zhu, 'Hallo3: Highly dynamic and realistic portrait image animation with video diffusion transformer,' in CVPR , 2025.

S. Qian, Z. Tu, Y. Zhi, W. Liu, and S. Gao, 'Speech drives templates: Co-speech gesture synthesis with learned templates,' in ICCV , 2021.

B. M.-K. Ng, S. R. Sudhoff, H. Li, J. Kamphuis, T. Nadolsky, Y. Chen, K. Y.-J. Yun, and Y.-H. Lu, 'Visualize music using generative arts,' in CAI , 2024.

Z. Yang, J. Teng, W. Zheng, M. Ding, S. Huang, J. Xu, Y. Yang, W. Hong, X. Zhang, G. Feng, et al. , 'CogVideox: Text-to-video diffusion models with an expert transformer,' in ICLR , 2025.

P. Esser, J. Chiu, P. Atighehchian, J. Granskog, and A. Germanidis, 'Structure and contentguided video synthesis with diffusion models,' in ICCV , 2023.

A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. English, V. Voleti, A. Letts, et al. , 'Stable video diffusion: Scaling latent video diffusion models to large datasets,' arXiv preprint arXiv:2311.15127 , 2023.

Y. He, T. Yang, Y. Zhang, Y. Shan, and Q. Chen, 'Latent video diffusion models for high-fidelity long video generation,' arXiv preprint arXiv:2211.13221 , 2022.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, 'High-resolution image synthesis with latent diffusion models,' in CVPR , 2022.

T. Soo Kim and A. Reiter, 'Interpretable 3D human action analysis with temporal convolutional networks,' in CVPR workshops , 2017.

G. Bertasius, H. Wang, and L. Torresani, 'Is space-time attention all you need for video understanding?,' in ICML , 2021.

O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional networks for biomedical image segmentation,' in MICCAI , 2015.

Y. Liu, K. Zhang, Y. Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y. Huang, H. Sun, J. Gao, et al. , 'Sora: A review on background, technology, limitations, and opportunities of large vision models,' arXiv preprint arXiv:2402.17177 , 2024.

W. Peebles and S. Xie, 'Scalable diffusion models with transformers,' in ICCV , 2023.

L. Yu, J. Lezama, N. B. Gundavarapu, L. Versari, K. Sohn, D. Minnen, Y. Cheng, V. Birodkar, A. Gupta, X. Gu, A. G. Hauptmann, B. Gong, M.-H. Yang, I. Essa, D. A. Ross, and L. Jiang, 'Language model beats diffusion - tokenizer is key to visual generation,' in ICLR , 2024.

A. Wang, B. Ai, B. Wen, C. Mao, C.-W. Xie, D. Chen, F. Yu, H. Zhao, J. Yang, J. Zeng, et al. , 'Wan: Open and advanced large-scale video generative models,' arXiv preprint arXiv:2503.20314 , 2025.

W. Kong, Q. Tian, Z. Zhang, R. Min, Z. Dai, J. Zhou, J. Xiong, X. Li, B. Wu, J. Zhang, et al. , 'Hunyuanvideo: A systematic framework for large video generative models,' arXiv preprint arXiv:2412.03603 , 2024.

G. Ma, H. Huang, K. Yan, L. Chen, N. Duan, S. Yin, C. Wan, R. Ming, X. Song, X. Chen, et al. , 'Step-Video-T2V technical report: The practice, challenges, and future of video foundation model,' arXiv preprint arXiv:2502.10248 , 2025.

M. Chatterjee and A. Cherian, 'Sound2Sight: Generating visual dynamics from sound and context,' in ECCV , 2020.

G. Le Moing, J. Ponce, and C. Schmid, 'CCVS: Context-aware controllable video synthesis,' in NeurIPS , 2021.

Y. Jeong, W. Ryoo, S. Lee, D. Seo, W. Byeon, S. Kim, and J. Kim, 'The power of sound (TPoS): Audio reactive video generation with stable diffusion,' in ICCV , 2023.

L. Zhang, S. Mo, Y. Zhang, and P. Morgado, 'Audio-synchronized visual animation,' in ECCV , 2024.

J. Jiang, C. Liang, J. Yang, G. Lin, T. Zhong, and Y. Zheng, 'Loopy: Taming audio-driven portrait avatar with long-term motion dependency,' in ICLR , 2025.

H. Wei, Z. Yang, and Z. Wang, 'AniPortrait: Audio-driven synthesis of photorealistic portrait animation,' arXiv preprint arXiv:2403.17694 , 2024.

W. Zhang, X. Cun, X. Wang, Y. Zhang, X. Shen, Y. Guo, Y. Shan, and F. Wang, 'SadTalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,' in CVPR , 2023.

G. Lin, J. Jiang, C. Liang, T. Zhong, J. Yang, and Y. Zheng, 'CyberHost: A one-stage diffusion framework for audio-driven talking body generation,' in ICLR , 2025.

G. Lin, J. Jiang, J. Yang, Z. Zheng, and C. Liang, 'OmniHuman-1: Rethinking the scaling-up of one-stage conditioned human animation models,' arXiv preprint arXiv:2502.01061 , 2025.

W. Xuanchen, W. Heng, L. Dongnan, and W. Cai, 'Dance any beat: Blending beats with visuals in dance video generation,' in WACV , 2025.

Z. Chen, H. Xu, G. Song, Y. Xie, C. Zhang, X. Chen, C. Wang, D. Chang, and L. Luo, 'Xdancer: Expressive music to human dance video generation,' arXiv preprint arXiv:2502.17414 , 2025.

P. Esser, R. Rombach, and B. Ommer, 'Taming transformers for high-resolution image synthesis,' in CVPR , 2021.

T. Karras, S. Laine, and T. Aila, 'A style-based generator architecture for generative adversarial networks,' in CVPR , 2019.

D. Jeong, S. Doh, and T. Kwon, 'Träumerai: Dreaming music with stylegan,' arXiv preprint arXiv:2102.04680 , 2021.

K. Soomro, A. R. Zamir, and M. Shah, 'Ucf101: A dataset of 101 human actions classes from videos in the wild,' arXiv preprint arXiv:1212.0402 , 2012.

S. Hong, W. Im, and H. S. Yang, 'Content-based video-music retrieval using soft intra-modal structure constraint,' arXiv preprint arXiv:1704.06761 , 2017.

J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, 'AudioSet: An ontology and human-labeled dataset for audio events,' in ICASSP , 2017.

J. S. Chung, A. Nagrani, and A. Zisserman, 'VoxCeleb2: Deep speaker recognition,' arXiv preprint arXiv:1806.05622 , 2018.

H. Chen, W. Xie, A. Vedaldi, and A. Zisserman, 'VggSound: A large-scale audio-visual dataset,' in ICASSP , 2020.

M. Bain, A. Nagrani, G. Varol, and A. Zisserman, 'Frozen in time: A joint video and image encoder for end-to-end retrieval,' in ICCV , 2021.

Y. Wang, Y. He, Y. Li, K. Li, J. Yu, X. Ma, X. Li, G. Chen, X. Chen, Y. Wang, et al. , 'InternVid: A large-scale video-text dataset for multimodal understanding and generation,' in ICLR , 2024.

H. Zhu, W. Wu, W. Zhu, L. Jiang, S. Tang, L. Zhang, Z. Liu, and C. C. Loy, 'CelebV-HQ: A large-scale video facial attributes dataset,' in ECCV , 2022.

W. Wu, M. Liu, Z. Zhu, X. Xia, H. Feng, W. Wang, K. Q. Lin, C. Shen, and M. Z. Shou, 'MovieBench: A hierarchical movie level dataset for long video generation,' arXiv preprint arXiv:2411.15262 , 2024.

M. Bain, A. Nagrani, A. Brown, and A. Zisserman, 'Condensed movies: Story based retrieval with contextual embeddings,' in ACCV , 2020.

R. Ghermi, X. Wang, V. Kalogeiton, and I. Laptev, 'Short film dataset (SFD): A benchmark for story-level video understanding,' arXiv preprint arXiv:2406.10221 , 2024.

H. Chen, Y. Zhang, X. Cun, M. Xia, X. Wang, C. Weng, and Y. Shan, 'VideoCrafter2: Overcoming data limitations for high-quality video diffusion models,' in CVPR , 2024.

B. Castellano, 'Video cut detection and analysis tool.' https://github.com/Breakthrough/PySceneDetect.

A. Tjandra, Y.-C. Wu, B. Guo, J. Hoffman, B. Ellis, A. Vyas, B. Shi, S. Chen, M. Le, N. Zacharov, et al. , 'Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound,' arXiv preprint arXiv:2502.05139 , 2025.

Y. Zhang, J. Wu, W. Li, B. Li, Z. Ma, Z. Liu, and C. Li, 'Video instruction tuning with synthetic data,' arXiv preprint arXiv:2410.02713 , 2024.

R. Solovyev, 'Cinematic sound demixing.' https://github.com/ZFTurbo/MVSEP-CDX23Cinematic-Sound-Demixing.

R. Hennequin, A. Khlif, F. Voituret, and M. Moussallam, 'Spleeter: a fast and efficient music source separation tool with pre-trained models,' Journal of Open Source Software , 2020.

G. Jocher, J. Qiu, and A. Chaurasia, 'Ultralytics YOLO.' https://github.com/ultralytics/ultralytics.

Elevenlabs, 'Meet scribe.' https://elevenlabs.io/blog/meet-scribe.

S. Beliaev and B. Ginsburg, 'TalkNet 2: Non-autoregressive depth-wise separable convolutional model for speech synthesis with explicit pitch and duration prediction,' arXiv preprint arXiv:2104.08189 , 2021.

A. Baevski, H. Zhou, A. Mohamed, and M. Auli, 'wav2vec 2.0: a framework for self-supervised learning of speech representations,' in NeurIPS , 2020.

D. P. Kingma and J. Ba, 'Adam: A method for stochastic optimization,' arXiv preprint arXiv:1412.6980 , 2014.

T. Unterthiner, S. Van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly, 'Towards accurate generative models of video: A new metric & challenges,' arXiv preprint arXiv:1812.01717 , 2018.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , 'Learning transferable visual models from natural language supervision,' in ICML , 2021.

J. Wang, C. Wang, K. Huang, J. Huang, and L. Jin, 'VideoCLIP-XL: Advancing long description understanding for video clip models,' arXiv preprint arXiv:2410.00741 , 2024.

R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, 'ImageBind: One embedding space to bind them all,' in CVPR , 2023.

J. S. Chung and A. Zisserman, 'Out of time: automated lip sync in the wild,' in ACCV Workshops , 2017.

S. Chen, Y. Wu, C. Wang, S. Liu, D. Tompkins, Z. Chen, W. Che, X. Yu, and F. Wei, 'BEATs: Audio pre-training with acoustic tokenizers,' in ICML , 2023.

M. Cai, X. Cun, X. Li, W. Liu, Z. Zhang, Y. Zhang, Y. Shan, and X. Yue, 'DitCtrl: Exploring attention control in multi-modal diffusion transformer for tuning-free multi-prompt longer video generation,' arXiv preprint arXiv:2412.18597 , 2024.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a new method to adapt video generation models for audio-to-video synthesis. It assumes that conditional audio is given as three separate tracks: speech, sound effects, and music. Under this setting, the proposed model extracts features from the three tracks via Multi-stream Temporal ControlNet and feeds them into the pretrained video generation model through additional cross-attention and feature modulation depending on the type of track. To train and evaluate the model, the authors created a new dataset comprising video, text, and demixed audio tracks. In the experiments, the proposed model outperforms prior audio-to-video models in terms of both video quality and audio-visual alignment.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The idea of using demixed audio for audio-to-video synthesis is interesting and convincing. \n  - Explicitly handling such separate audios should enable fine-grained controls to the video generation process, because how conditioning audio impacts on the generated videos should vary depending on the type of the audio.\n- The dataset construction process seems to be thorough, and the release of this dataset would benefit a lot the community in the field of audio-visual generation.\n- The proposed model empirically outperforms prior audio-to-video models significantly. I have checked the supplementary video and the performance gain is also obvious in perceptual quality.\n- The manuscript is well-written and easy to follow.\n\n## Weaknesses\n\n- I have a concern regarding license or copyright issues on the film-related videos mentioned in line 111.\n  - This is because the dataset construction process shown in Section 3 seems not to contain a filtering process to eliminate this issue.\n- I do not find crucial weaknesses regarding the design of the proposed method and the experiments. Shown below are just a few suggestions to strengthen the significance of this study rather than weaknesses. It is completely fine to ignore them during the rebuttal phase.\n  - In the proposed model, the extracted features \\tilde{f} are commonly used for every layer of DiT, and this design is essentially different from the standard setting of ControlNet for DiT adopted in popular models such as Stable Diffusion 3.5 and PixArt-delta, where the features are extracted in a layer-wise manner. I understand that the standard design cannot be directly applied to the case in this study due to the domain gap between video and conditional audio, but it would be worth trying layer-adaptive feature extraction also in the proposed model. I personally suspect that the above-mentioned design could contribute the trade-off mentioned in Section A.2.\n  - Regarding the generalizability, it would be great if the proposed method also works with more recent video diffusion models such as Wan, but I understand that trying this could require prohibitively large computational cost for training.\n\nQUESTIONS:\n- Did the authors conduct any filtering process regarding license or copyright of the video when constructing the dataset?\n\nLIMITATIONS:\nThe limitations of the proposed method are briefly discussed in Section 6.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MTV for generating video synchronized with audio input by explicitly separating audio into three tracks: speech, effects, and music. This separation allows precise control over lip motion, event timing, and visual mood. \nIn addition, the authors introduce DEMIX, a new large-scale dataset composed of cinematic video clips paired with demixed audio. \nThe paper also proposes a new component, the Multi-Stream Temporal ControlNet (MST-ControlNet), designed to handle these separate audio streams and integrate their signals into a text-to-video diffusion model. The paper reports state-of-the-art results across several metrics on audio-sync video generation, including video quality, audio-video synchronization, and consistency with text descriptions. The authors also provide extensive ablations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper targets a meaningful and useful gap in video generation and audio-video synchronization, especially in the field of cinematic video.\n2. This paper builds a new dataset, DEMIX, with a large-scale and careful filtering, which is a solid contribution to the community. \n3. The paper provides remarkable quantitative and qualitative results over three baseline models, indicating the effectiveness of the proposed method.\n4. Paper writing is clear and easy to understand.\n\nWeaknesses:\n1. The proposed model is heavily dependent on a list of existing models. For example: \n(1) Audio track separation has a significant impact on the model's overall performance (Tab 2 ablation study), while it is heavily bottlenecked by the demixing tools (MVSEP, Spleeter). The authors didn't provide failure cases when audio is noisy and is not cleanly separable, which is quite common in real-world scenarios. \n(2) The proposed model applies CogVideoX as base model, wav2vec for audio encoding, and T5 for text embedding. I believe the novelty of architecture mainly lies in module assembling. Given that, it is unclear how much of the performance gain comes from the proposed method instead of pretrained models. More ablations on choosing different pre-existing models are suggested.\n(3) Given the complicated pipeline that includes multiple external models, I have concerns on model's reproducibility.\n2. From my understanding, the text template is fixed (Sec 4.3), which lacks generalizability when the scenario is complicated to describe. There lacks such examples (e.g, 3 or more people) discussed in the paper. There lack of discussion on limitations of text descriptions, which I believe has a high impact on the overall performance and control quality. \n3. Concerns about comparison fairness. It is unclear to me if the other 2 baselines (TempoTokens and Xing et al.) are retrained on the DEMIX dataset or not. Without consistent training and evaluation approaches, the fairness of performance comparisons is questionable. Experiments on public standard benchmark are suggested.\n\nQUESTIONS:\n1. How are the other 2 baselines (TempoTokens and Xing et al.) evaluated? Are they retrained on the DEMIX dataset?\n2. Could the authors provide more details on the fine-tuning of MM-Diffusion? Does it follow the same multi-stage training process on all five subsets of DEMIX? \n3. Could the authors provide examples where the audio demixing is not perfect and show how this negatively impacts the final video?\n4. Could the authors provide examples with multiple people and complicated scenarios for text descriptions?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents MTV, a unified and controllable framework for audio-synchronized video generation. The framework is supported by a newly introduced dataset, DEMIX, which includes high-quality cinematic videos with corresponding audio tracks and is structured to support scalable training across varied video generation tasks. \nThe proposed approach demonstrates state-of-the-art performance in terms of video quality, text-video consistency, and audio-video alignment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n\n- The work is overall very clearly written and expressed. \n\n- The author provides a new dataset DEMIX, and the corresponding view and experiment is supported\n\nWeaknesses\n\n- The embeddings are uniformly applied to all frames via global style injection to control visual emotion (Lines 56–57).\nIs such global control reasonable? For long-sequence video modeling, mood distribution may not be uniform over time.\nThe authors are advised to provide a proper reference for the statement \"Since visual mood typically covers the entire video clip\" instead of relying on subjective assumptions. \n\n- The \"multi-stream temporal ControlNet\" essentially injects audio information into the DiT via cross-attention. This approach appears overly simplistic and lacks novelty.\n\n- The paper emphasizes the extraction of features from the music track to enhance visual mood. However, the experimental section does not include any objective evaluation metrics related to emotion or mood to support this claim.\n\nQUESTIONS:\n1. Is it reasonable to use the same Wav2vec encoder to process all types of audio signals (speech, sound effects, and music)?\nWav2vec is primarily designed to extract semantic representations from speech. While this may be effective for speech, the authors should consider whether using alternative encoders for sound effects and music could yield better performance or more meaningful features.\n\n2. In the proposed \"multi-stream temporal ControlNet\", I am particularly curious why speech and sound effects are mixed as input.\nIn clean talking-head scenarios, sound effects may interfere with accurate lip synchronization.\nThe authors should validate this design choice and provide a reasonable explanation for combining these audio streams.\n\n3. How does the method handle multiple speakers? (Figure 1, second row). How are different speech signals distinguished and mapped to corresponding face regions? Would this setup lead to interference in visual information when speech overlaps or when there is temporal misalignment?\n\n4. Is it sufficient to divide the input audio signal into only three components: speech, sound effects, and music?\nThe current categorization overlooks other potentially important audio modalities such as ambient sounds and reverb/spatial cues. \n\nIf the authors can provide clear and detailed responses to the concerns raised, I would be willing to reconsider and potentially raise my overall score.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MTV framework (Multi-stream Temporal control for audio-sync Video generation), a framework for generating high-fidelity cinematic videos with precise audio-visual synchronization across diverse audio types. Existing methods struggle with under-specified audio-visual mapping and inaccurate temporal alignment. MTV addresses these challenges by separating audio inputs into speech, effects and music tracks, for disentangled control over lip motion, event timing, and visual mood, enabling fine-grained and semantically aligned video generation. To support this framework, the authors present an audio-sync video generation dataset, DEMIX, structured into five overlapped subsets and introduce multi-stage training strategy. Additionally, the paper proposes MST-ControlNet (Multi-Stream Temporal ControlNet) to distinctively process demixed audio stream and inject audio features of each track into corresponding time intervals, achieving fine-grained synchronization. The performance of MTV for video quality, text-video consistency, and audio-video alignment outperforms 3 baseline methods across six standard metrics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- MTV method is directly aligned with the two core motivations--under-specified audio-visual mapping and inaccurate temporal alignment--by explicitly demixing audio into distinct controlling tracks with dedicated temporal control mechanisms.\n- The authors contribute DEMIX, a large-scale cinematic video clips with demixed audio tracks, structured into five overlapping subsets, which facilitates multi-stage training.\n\nWeaknesses\n- MTV's performance depends on the accuracy of upstream demixerrs (MVSEP, Spleeter). Errors in source separation can propagate and degrade synchronization.\n- Quantitative results are reported only on DEMIX dataset and CogVideoX baseline model. It remains unclear how well MTV generalizes to other T2V models and different datasets (e.g., non-cinematic content)\n- The paper relies solely on automated metrics; incorporating qualitative human assessments would better validate diverse aspects including detailed semantic consistency, narrative fluency of video, and user preferences.\n\nQUESTIONS:\n- Robustness to imperfect demixing isn't evaluated. Have you evaluated synchronization quality under varying levels of demixing noise?\n- Can MTV maintain its performance gain when it is applied on various audio-visual datasets or other state-of-the-art video-diffusion models? (See weakness 2)\n\nLIMITATIONS:\n- MTV generates audio-sync videos only at a 480 x 720 resolution and 49-frame segments, limiting its scalability to high-resolution or long-form video generation.\n- The paper lacks ablations or empirical justification for key design choices (e.g., stream interaction blocks, training schedule).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}