A Synchronized Audio-Visual Multi-View Capture System
- URL: http://arxiv.org/abs/2603.23089v1
- Date: Tue, 24 Mar 2026 11:32:49 GMT
- Title: A Synchronized Audio-Visual Multi-View Capture System
- Abstract summary: We describe an audio-visual multi-view capture system that treats synchronized audio and synchronized video as first-class signals.<n>The resulting recordings are temporally consistent enough to support fine-grained analysis and data-driven modeling of conversation behavior.
- Score: 2.6985892552916995
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Multi-view capture systems have been an important tool in research for recording human motion under controlling conditions. Most existing systems are specified around video streams and provide little or no support for audio acquisition and rigorous audio-video alignment, despite both being essential for studying conversational interaction where timing at the level of turn-taking, overlap, and prosody matters. In this technical report, we describe an audio-visual multi-view capture system that addresses this gap by treating synchronized audio and synchronized video as first-class signals. The system combines a multi-camera pipeline with multi-channel microphone recording under a unified timing architecture and provides a practical workflow for calibration, acquisition, and quality control that supports repeatable recordings at scale. We quantify synchronization performance in deployment and show that the resulting recordings are temporally consistent enough to support fine-grained analysis and data-driven modeling of conversation behavior.
Related papers
- Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention [31.052048543229464]
We present a synchronization-aware acceleration framework for efficient audio-visual generation.<n>Our method improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.
arXiv Detail & Related papers (2026-08-16T04:26:05Z) - MAVIN: Multi-Shot Audio-Visual Generation with Narrative Control [66.04301887685004]
Existing methods suffer from temporal misalignment, limited controllability, and incomplete scripting.<n>We propose MAVIN, the first framework for multi-shot audio-visual generation with customized narrative control.<n> MAVIN achieves state-of-the-art performance, opening up a new avenue for integrating generative models into professional filmmaking.
arXiv Detail & Related papers (2026-06-28T16:01:04Z) - StreamChar: Long-Horizon Streaming Character Audio-Video Generation with Decoupled Orchestration [16.23723735702324]
We present StreamChar, a streaming framework that separates long-horizon orchestration from short-window audio-video denoising.<n>Experiments on short-clip and long-horizon protocols show that StreamChar runs in real time on a single H100 GPU.
arXiv Detail & Related papers (2026-05-25T10:04:52Z) - Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows [75.44753202066171]
Coordinated audio generation based on video inputs typically requires a strict audio-visual (AV) alignment.<n>We propose FoleyFlow to first align unimodal AV encoders via masked modeling training.<n>After training, the AV encoders which are separately pretrained using only unimodal data are aligned with semantic and rhythmic consistency.
arXiv Detail & Related papers (2026-03-09T09:06:25Z) - Syncphony: Synchronized Audio-to-Video Generation with Diffusion Transformers [19.226787997122987]
We present Syncphony, which generates 380x640 resolution, 24fps videos synchronized with diverse audio inputs.<n>Our approach builds upon a pre-trained video backbone and incorporates two key components to improve synchronization.<n>Experiments on AVSync15 and The Greatest Hits datasets demonstrate that Syncphony outperforms existing methods in both synchronization accuracy and visual quality.
arXiv Detail & Related papers (2025-09-26T05:30:06Z) - SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers [25.36460340267922]
We present SkyReels-Audio, a unified framework for synthesizing high-fidelity and temporally coherent talking portrait videos.<n>Our framework supports infinite-length generation and editing, while enabling diverse and controllable conditioning through multimodal inputs.
arXiv Detail & Related papers (2025-06-01T04:27:13Z) - Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition [31.25956665297592]
We decompose the mel-spectrogram into three distinct types of signals, employing quantization or continuity to them.<n>We can effectively predict them from video by a devised video-to-all (V2X) predictor.<n>Then, the predicted signals are recomposed and fed into a ControlNet, along with a textual inversion design, to control the audio generation process.
arXiv Detail & Related papers (2025-03-10T07:04:03Z) - Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge [57.01131456894516]
Current video understanding models struggle with processing long video sequences, supporting multi-turn dialogues, and adapting to real-world dynamic scenarios.<n>We propose StreamChat, a training-free framework for streaming video reasoning and conversational interaction.<n>Our framework incorporates a parallel system scheduling strategy that enhances processing speed and reduces latency, ensuring robust performance in real-world applications.
arXiv Detail & Related papers (2025-01-23T08:33:10Z) - Audio-Visual Talker Localization in Video for Spatial Sound Reproduction [3.2472293599354596]
In this research, we detect and locate the active speaker in the video.
We found the role of the two modalities to complement each other.
Future investigations will assess the robustness of the model in noisy and highly reverberant environments.
arXiv Detail & Related papers (2024-06-01T16:47:07Z) - Synchformer: Efficient Synchronization from Sparse Cues [100.89656994681934]
Our contributions include a novel audio-visual synchronization model, and training that decouples extraction from synchronization modelling.
This approach achieves state-of-the-art performance in both dense and sparse settings.
We also extend synchronization model training to AudioSet a million-scale 'in-the-wild' dataset, investigate evidence attribution techniques for interpretability, and explore a new capability for synchronization models: audio-visual synchronizability.
arXiv Detail & Related papers (2024-01-29T18:59:55Z) - Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities [67.89368528234394]
One of the main challenges of multimodal learning is the need to combine heterogeneous modalities.
Video and audio are obtained at much higher rates than text and are roughly aligned in time.
Our approach achieves the state-of-the-art on well established multimodal benchmarks, outperforming much larger models.
arXiv Detail & Related papers (2023-11-09T19:15:12Z) - STELLA: Continual Audio-Video Pre-training with Spatio-Temporal Localized Alignment [61.83340833859382]
Continuously learning a variety of audio-video semantics over time is crucial for audio-related reasoning tasks.
This is a nontemporal problem and poses two critical challenges: sparse-temporal correlation between audio-video pairs and multimodal correlation overwriting that forgets audio-video relations.
We propose a continual audio-video pre-training method with two novel ideas.
arXiv Detail & Related papers (2023-10-12T10:50:21Z) - Sparse in Space and Time: Audio-visual Synchronisation with Trainable
Selectors [103.21152156339484]
The objective of this paper is audio-visual synchronisation of general videos 'in the wild'
We make four contributions: (i) in order to handle longer temporal sequences required for sparse synchronisation signals, we design a multi-modal transformer model that employs'selectors'
We identify artefacts that can arise from the compression codecs used for audio and video and can be used by audio-visual models in training to artificially solve the synchronisation task.
arXiv Detail & Related papers (2022-10-13T14:25:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.