Visual-Guided Audio Spatialization

• Visual-guided audio spatialization is a module that maps visual cues such as object position, saliency, and geometry to spatial audio parameters like ITD/ILD and HRTF for immersive sound synthesis.
• The approach combines data-driven neural architectures, including transformer and U-Net models, with physics-based geometric renderers to generate binaural, stereophonic, or ambisonic audio.
• Implications involve enhancing AR/VR experiences and multimedia production by enabling realistic, scene-consistent spatial audio while addressing challenges in sound source disentanglement.

A visual-guided audio spatialization module is a computational framework that exploits visual signals (either images, video, or geometric scene data) to predict or synthesize spatial audio fields, such as binaural, stereophonic, or ambisonic representations, from video, mono audio, or silent visual streams. These modules operationalize the process of mapping visual cues—including object position, saliency, semantic information, geometric layout, and camera orientation—onto spatial parameters of an audio field (e.g., ITD/ILD, spherical harmonic coefficients, or HRTF-processed waveforms), creating plausible, scene-consistent multi-channel audio suitable for immersive applications. Approaches span data-driven neural models leveraging cross-modal encodings and physics-based geometrical renderers linked to visual scene understanding. The following sections detail technical foundations, leading architectures, training paradigms, mathematical models, and evaluation criteria in visual-guided audio spatialization, with reference to recent primary literature.

1. Technical Foundations and Problem Formulation

At its core, visual-guided audio spatialization addresses the ill-posed problem of inferring the multidimensional parameters of an acoustic field from visual scene data, optionally aided by mono audio. The canonical formulation is: Given a sequence of synchronized video frames $V_t$ and (optionally) audio $x_t$ and/or camera pose data $D=(\varphi, \theta)$, generate a plausible multi-channel spatial audio waveform $A$ (e.g., FOA: $A \in \mathbb{R}^{4 \times L}$, binaural $A \in \mathbb{R}^{2 \times L}$) that reflects the geometry, object arrangement, and real or plausible sound source positions visible in the video (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026, Liu et al., 11 Feb 2025).

Different tasks instantiate this problem:

• Silent video $\rightarrow$ spatial audio: Generate (possibly semantic) spatial audio from video frames only (Kim et al., 13 Jun 2025).
• Mono video+audio $\rightarrow$ spatial audio: Convert mono recordings with visual guidance to binaural/stereophonic/ambisonic (Garg et al., 2021, Zhou et al., 2020).
• Spatialization for multi-agent/scene labels: Assign separate audio sources to detected objects/regions, based on their visual properties and scene layout (Liu et al., 11 Feb 2025, Zhao et al., 18 Aug 2025, Berghi et al., 2024).
• Real-time spatial sound rendering: Streaming scene-synchronized spatial audio for AR/VR, based on live camera pose and visual feeds (Chen et al., 2022, Kim et al., 13 Jun 2025).

2. Architectures and Computational Strategies

Visual-guided audio spatialization modules can be structurally grouped into the following paradigms:

Data-Driven Neural Architectures

• Transformer-based encoder–decoders: Exampled by ViSAGe, which leverages a CLIP ViT-B/32 image encoder for per-frame features, computes patchwise spatial saliency, incorporates camera direction, and uses a cross-modal transformer encoder with an autoregressive transformer decoder for discrete neural codec codes representing FOA ambisonics (Kim et al., 13 Jun 2025).
• U-Net and associative fusion: Sep-Stereo and related models employ U-Net backbones with audio–visual feature fusion at multiple spatial scales, e.g., the multi-scale Associative Pyramid Network (APNet), which enables region-specific alignment between visual features and latent/unmixed audio elements (Zhou et al., 2020).
• Multi-task networks: Geometry-aware spatializers (e.g., (Garg et al., 2021)) enforce explicit regularization using auxiliary losses for RIR prediction, audio–visual spatial coherence (channel flipping), and temporal geometry consistency, providing regularized visual features for audio mask prediction.
• GAN and flow-matching frameworks: Cross-modal GANs (e.g., SAGM) apply adversarial training with shared/alternated visual guidance between generator and discriminator, optimizing both spatial realism and audio–visual correspondence as measured by SPL-difference metrics (Li et al., 2023). SpatialV2A introduces conditional flow matching to synthesize binaural streams modulated by frame-level spatial cues (Wang et al., 21 Jan 2026).

Geometry- and Physics-Based Approaches

• Path tracing and acoustic simulation: SoundSpaces 2.0 renders spatial audio by simulating wave propagation using a bidirectional path-tracer over a 3D mesh, associating each spatial audio impulse response to an explicit sound-path determined by scene geometry and material acoustics extracted from visual mesh data (Chen et al., 2022).
• Visual-metadata-driven spatial renderers: Audio-visual talker localization modules combine speaker detection in video (face detection, 2D$\rightarrow$3D correspondence) with array-based TDoA localization and metadata fusion, providing precise spatiotemporal trajectories for subsequent binaural, VBAP, or ambisonic rendering (Berghi et al., 2024).
• Coordinate-mapping spatialization: FoleySpace demonstrates framewise 2D detection (open-vocabulary object detector), monocular depth, and custom coordinate mapping to reconstruct dense 3D trajectories, which—together with a monaural audio track—condition diffusion models to generate spatially coherent binaural audio (Zhao et al., 18 Aug 2025).

3. Input Modalities, Feature Extraction, and Conditioning

Across the literature, the following feature extraction and conditioning components are prominent:

• Visual encoders: ViT/B, CLIP, ResNet-18/50, C3D, YOLO-based detectors for object/person identification and position estimation. Some models utilize patchwise attention or energy/saliency maps to enhance localization of visually prominent sources (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026).
• Spatial saliency computation: Either via pre-pooling ViT features (cosine similarity over spatial/temporal neighbors—ViSAGe) or ACL-based sound source heatmaps, projected into spatial conditioning vectors or tensors (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026).
• Camera orientation and trajectory information: Encoded as sequence- or frame-level embeddings, sometimes directly modulating cross-attention or code prediction (ViSAGe, SoundSpaces) (Kim et al., 13 Jun 2025, Chen et al., 2022).
• Audio encoders: Mono or mixture STFT, VAE encoders, hierarchical audio U-Nets for extracting suitable input latents to decoders or generators (Garg et al., 2021, Zhou et al., 2020, Li et al., 2023).
• Auxiliary metadata (object depth, source separation): Face detection and depth estimation for multi-source mapping (YOLOv8, DepthAnything, DepthMaster) (Liu et al., 11 Feb 2025, Zhao et al., 18 Aug 2025).

A typical visual-guided spatialization pipeline fuses these through cross-modal concatenation, associative convolution, or explicit injection into transformer/self-attention or GAN blocks.

4. Mathematical Modeling and Training Objectives

The generative process is mathematically formalized through:

• Spherical harmonic expansion of fields: FOA channels correspond to orthonormal basis functions on the sphere, e.g., $$G(\varphi, \theta) = \frac{1}{L}\sum_{t} [Y_0^0(\varphi, \theta)W(t) + Y_{-1}^1(\varphi, \theta)Y(t) + Y_0^1(\varphi, \theta)Z(t) + Y_1^1(\varphi, \theta)X(t)]$$ (Kim et al., 13 Jun 2025).
• Patchwise saliency and energy mapping: $$S_{ij}^t=2-2\,\cos(x_{ij}^t, \overline{x}_{kl}),\,E^t=\operatorname{softmax}(S^t+T^t)$$ for temporal and spatial visual attention (Kim et al., 13 Jun 2025).
• Spatial conditioning vectors: Horizontal centroid, sound area, variance, left-right bias, anisotropy, assembled from heatmaps for each frame and mapped to conditioning tensors (Wang et al., 21 Jan 2026).
• Losses: Cross-entropy over discrete codec codes, CFM loss, L2/1 reconstruction (on waveform, spectrogram, binaural difference), KL divergence between predicted and ground-truth localization features, binary cross-entropy for spatial coherence, adversarial (GAN) losses (Kim et al., 13 Jun 2025, Li et al., 2023, Wang et al., 21 Jan 2026, Garg et al., 2021).

Self-supervised and semi-supervised learning strategies, involving audio–visual consistency and co-attention, further reduce dependence on ground-truth spatialized data (Lin et al., 2021, Zhou et al., 2020).

5. Evaluation Metrics and Benchmarking

Major works introduce and adopt specialized metrics for spatialization fidelity:

• Fréchet Audio Distance (FAD): Computed either on mono (decoded from spatial audio via downmixing) or directly on multi-channel (Kim et al., 13 Jun 2025, Zhao et al., 18 Aug 2025).
• KL divergence on classifier outputs: Using AudioSet, PANNs, or PaSST model output distributions for semantic/temporal congruence (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026).
• Correlation and AUC on energy maps: Time-resolved (1 s, 0.2 s window) correlation coefficient and ROC-AUC between generated and ground-truth directional energy heatmaps, with sin θ equirectangular correction (Kim et al., 13 Jun 2025).
• Perceptual quality: MOSNet, PESQ, STOI, and envelope distance (Liu et al., 11 Feb 2025, Lin et al., 2021, Li et al., 2023).
• Spatial accuracy: SPL-difference (magnitude/direction) over time, IACC, ITD/ILD/ISD/IPD distributions, and subjective MOS–Spatial (Li et al., 2023, Wang et al., 21 Jan 2026, Zhao et al., 18 Aug 2025).
• Benchmark datasets: FAIR-PLAY, YT-MUSIC, YT-Ambigen, BinauralVGGSound, SimBinaural, VGGSound-Solo, Tragic Talkers (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026, Garg et al., 2021, Berghi et al., 2024).

6. Practical Applications and Generalization

Visual-guided audio spatialization modules are foundational in:

• Immersive multimedia production: Automatic video spatialization for AR/VR, films, gaming (real-time soundfields in ViSAGe, FoleySpace, SoundSpaces) (Kim et al., 13 Jun 2025, Zhao et al., 18 Aug 2025, Chen et al., 2022).
• Scene understanding and embodied navigation: Audio-visual navigation, map building, source–trajectory estimation, continuous localization (Chen et al., 2022, Berghi et al., 2024).
• Binauralization of mono video/audio: Data-driven and physics-based approaches enable consistent spatialization even for legacy or synthetic content (Garg et al., 2021, Zhou et al., 2020, Lin et al., 2021, Li et al., 2023, Liu et al., 11 Feb 2025).
• Flexible scaling: Many models (ViSAGe, SoundSpaces, FoleySpace) can be adapted to higher-order ambisonics, custom microphone arrays, real-time spatial effects, and multiple scene representations with minimal architectural change (Kim et al., 13 Jun 2025, Chen et al., 2022, Zhao et al., 18 Aug 2025).

A plausible implication is that modularity in fusion, conditioning, and codec/decoder design is critical for extensibility across target spatial formats and application domains.

7. Summary of State-of-the-Art and Open Issues

Recent visual-guided audio spatialization research demonstrates that transformer-centric, cross-modal fusion architectures driven by saliency-aware visual backbones and explicit spatial conditioning achieve significant gains in realism, spatial fidelity, and temporal alignment over prior two-stage or audio-only methods (Kim et al., 13 Jun 2025, Wang et al., 21 Jan 2026, Li et al., 2023). Geometry-based renderers remain crucial for high-fidelity, sim-to-real learning and physically grounded applications (Chen et al., 2022, Berghi et al., 2024).

A persistent challenge is robust disentanglement of sound sources, especially in dense or occluded multi-object scenes—an area in which data augmentation, fusion fallback (e.g., under occlusion), and improved visual–audio alignment losses are active research foci (Berghi et al., 2024, Liu et al., 11 Feb 2025). The generalization of modules to novel environments, dynamic camera motion, and new sensor setups is increasingly addressed by plug-in compatibility (Habitat-Sim integration, universal coordinate mapping, and off-the-shelf detectors) (Chen et al., 2022, Zhao et al., 18 Aug 2025).

Metrics for spatial perception—such as time-varying SPL-difference or energy map correlation—are emergent standards, facilitating objective, reproducible benchmarking between competing spatialization pipelines.

In sum, visual-guided audio spatialization modules are now architecturally and methodologically central to next-generation spatial audio synthesis. They offer a versatile bridge between visual scene understanding and high-fidelity, immersive spatial sound synthesis (Kim et al., 13 Jun 2025, Li et al., 2023, Zhao et al., 18 Aug 2025, Wang et al., 21 Jan 2026, Chen et al., 2022, Garg et al., 2021, Berghi et al., 2024, Liu et al., 11 Feb 2025, Zhou et al., 2020, Lin et al., 2021).