Map2Video: Interactive Geospatial Video Synthesis

• Map2Video is a system that synthesizes video from map-based geospatial inputs using street-view data and AI-driven video inpainting techniques.
• It integrates interactive map panels, timeline editing, and diffusion-based backends to ensure spatial and temporal coherence in video outputs.
• Evaluations indicate enhanced spatial accuracy, usability, and reduced cognitive load compared to traditional navigation and video generation methods.

Map2Video refers to a class of systems and methodologies for generating, navigating, or synthesizing video content directly and interactively from map-based geospatial inputs, often leveraging street-view data, AI-driven video generation models, or panoramic video capture. Contemporary approaches facilitate spatially consistent video synthesis and exploration, enabling applications from immersive city navigation to creative filmmaking grounded in real-world geography (Jo et al., 19 Dec 2025, Sugimoto et al., 2020).

1. System Architectures and Core Components

Map2Video systems span a continuum from pure data-driven navigation (e.g., omnidirectional video exploration interfaces) (Sugimoto et al., 2020) to advanced AI-guided video synthesis conditioned on real-world street-view imagery and user interaction (Jo et al., 19 Dec 2025). Recent Map2Video pipelines, such as Jo et al.'s 2025 system, employ a Unity-based front end for map-based interaction, combined with a ComfyUI-anchored back end hosting diffusion-based video generation models (Wan2.1 VACE 14B) and LoRA adapters for enhanced video inpainting.

Typical architectural components include:

• Map panel: Embeds interactive OpenStreetMap layers and overlays street-view panorama nodes for precise location or camera placement.
• Street View panel: Provides direct manipulation of pan/tilt/FOV within 360° imagery.
• Timeline and keyframes: Allow precise choreography of actors and cameras, facilitating scene planning in both spatial and temporal domains.
• Background-to-mask video pipeline: Generates background sequences and spatial masks matching actor/camera trajectories.
• Remote video generation backend: Orchestrates conditioning of diffusion models (via CLIP, VAE, upscalers) for synthesis of spatially and semantically aligned video output.

Integration of mapping APIs (e.g., Mapillary) is standard for creating high-fidelity geographical groundings (Jo et al., 19 Dec 2025).

2. Geospatial Transformations and Trajectory Representation

Accurate mapping from geodetic coordinates $(\varphi, \lambda)$ to video frame pixels is critical for spatial consistency. Map2Video systems establish a local East–North–Up (ENU) tangent plane anchored at the camera’s position. Displacements in latitude and longitude are linearized via:

$$\Delta x_{\text{east}} = R\cos\varphi_c\,(\lambda - \lambda_c),\quad \Delta z_{\text{north}} = R(\varphi - \varphi_c)$$

where $R$ is Earth’s mean radius.

Projection into the panorama frame leverages camera heading and pitch, transforming world-relative azimuth/elevation to normalized screen coordinates, and finally to pixel $(u,v)$ placements. Actor or camera trajectories, specified on the map as polylines, undergo Bézier or Catmull–Rom interpolation and reparameterization to form smooth, time-parameterized paths. Each interpolated trajectory point is projected at each video frame, enabling seamless mask placement and dynamic camera movement (Jo et al., 19 Dec 2025).

3. AI Video Generation and Conditioning Methods

Advanced Map2Video systems apply conditional video inpainting and synthesis models. For instance, the Wan2.1 VACE 14B backbone utilizes a multi-scale 3D U-Net architecture with spatiotemporal attention and cross-modal conditioning. The pipeline is structured as follows:

• Inputs: Upscaled background video, binary mask video, CLIP-encoded text prompt, and optional reference imagery.
• Conditioning: Video condition and mask tensors are injected into the diffusion U-Net, while prompt and reference encodings provide semantic and visual grounding.
• Sampling strategy: Employed is a ModelSamplingSD3 scheduler with specific guidance scales (e.g., 1.0) and LoRA-adapted regularizers for temporal consistency.
• Loss function: Minimizes denoising diffusion loss,

$$\mathcal{L}_{\mathrm{diff}} = \mathbb{E}_{z_0, t, \epsilon}\left[\left\| \epsilon - \epsilon_\theta(z_t, e, t) \right\|^2\right]$$

with optional temporal flow regularization.

This conditioning enforces spatial and temporal coherence between dynamically synthesized actors, prompts, and real-world street-view context (Jo et al., 19 Dec 2025).

4. Interactive Interfaces and User Workflows

Map2Video interfaces are explicitly designed for iterative, spatially informed video creation:

• Map-based location scouting: Users anchor camera and actor positions on interactive map layers, accessing corresponding street-level panoramas.
• Direct manipulation: Drag-and-drop UI for mask positioning and trajectory sketching; pan/tilt/zoom operations configurable per frame.
• Keyframe and timeline editing: Allows path and camera motion refinement, synchronizing user actions with scene-level shot design.
• Prompt-driven generation: Textual descriptions, optionally augmented with reference images, control actor identity and action during inpainting.
• Result playback and batch rendering: Generated clips are reviewed in an integrated video player and can be iteratively refined or manually stitched (Jo et al., 19 Dec 2025).

Earlier approaches, including Movie Map (Sugimoto et al., 2020), emphasize continuous navigation through a graph of road segments and intersections, with special handling for synchronized panoramic turns and overlayed virtual billboards.

5. Data Structures and Information Management

Efficient video navigation and synthesis rely on robust data management strategies:

• Graph structures: Nodes represent intersections; directed edges encode street segments and possible navigational transitions, including pre-synthesized turning-view mini-clips.
• Runtime lookup: Navigation events retrieve corresponding video segments, transitions, and virtual billboard overlays referenced by geospatial coordinates.
• Storage: Systems utilize SQL/JSON tabulation for graph data and segment indexing; clip caching supports low-latency playback.

The explicit modeling of scene topology facilitates granular traversal and editing while ensuring temporal and spatial coherence in multisegment video outputs (Sugimoto et al., 2020).

6. Evaluation Metrics and Comparative Performance

Map2Video systems are evaluated using both objective and subjective metrics:

• Spatial accuracy: Likert-rated consistency of generated clips (mean $6.5/7$ for Map2Video vs $4.4/7$ for a baseline) (Jo et al., 19 Dec 2025).
• Cognitive effort: NASA-TLX-derived scores indicate a 15% reduction in mental workload relative to standard pipelines.
• System usability: System Usability Scale (SUS) ratings classify Map2Video as “Excellent” (mean 83) compared to “Poor” (mean 51) under baseline conditions.
• Controllability: Users report significantly enhanced control over scene and shot design (mean $5.9/7$ for Map2Video).
• Task success and speed: Fewer failed navigation/replication attempts and up to 17% reduction in completion time are observed compared to standard image-to-video tools or Google Street View navigation (Sugimoto et al., 2020, Jo et al., 19 Dec 2025).

Statistical significance is established via paired t-tests (e.g., $p < 0.01$ for comfort and mental demand measures) and Cohen's $d$ values up to 2.4 indicate very large effects on user outcomes.

7. Limitations and Future Research Directions

Current limitations include:

• Camera translation: Only pan/tilt/zoom are supported per panorama node; no support for continuous dolly or crane shots due to discrete street-view sampling.
• Parallax and stylization: Discrete panorama data constrains lateral motion and stylization; output realism is limited by source image quality.
• UI coupling: Discontinuity between map and street view interaction can reduce creative fluidity in scene blocking (Jo et al., 19 Dec 2025).

Proposed advances target integration of multi-view reconstruction or panoramic NeRF techniques for genuine camera translation, multi-scale path editing, and AI-augmented design tools (e.g., stylization adapters, continuity auditing). Layout-conditioned diffusion and advanced scheduling could further improve realism, genre control, and affordance for creative exploration.

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Principal references: (Jo et al., 19 Dec 2025, Sugimoto et al., 2020).