Trajectory-Guided Panoramic Video Generation

Updated 16 August 2025

The paper introduces trajectory-guided panoramic video generation by integrating data-driven selection, deep diffusion models, and geometry-based methods to ensure spatial and temporal consistency.
Trajectory-guided panoramic video generation is defined as the synthesis of coherent 360° video content where virtual camera paths or object motions follow explicit, smooth trajectories using both statistical and deep learning techniques.
Key evaluations employ metrics like cosine similarity, FID/FVD, and trajectory error to measure alignment with human-edited paths and validate the physical plausibility of synthesized scenes.

Trajectory-guided panoramic video generation refers to the class of algorithms and frameworks tasked with synthesizing dynamic 360° video content where the camera’s viewpoint—or the motion of multiple scene elements—follows a prescribed spatio-temporal trajectory. These methods span conventional automatic cinematography in panoramic videos, deep video diffusion models with explicit trajectory conditioning, geometry-based projections guided by path, and unified entity/object-level controllers applicable to panoramic domains. The following sections summarize the technical principles, dominant methodologies, evaluation protocols, and practical implications as drawn from key works in the field.

1. Fundamental Problem and Scope

The core objective in trajectory-guided panoramic video generation is to translate either a static omnidirectional video (360° video), static image, text prompt, or overhead/satellite imagery into a spatio-temporally coherent panoramic (or NFOV) video where the camera or scene entities follow user-defined or automatically inferred trajectories. This “trajectory” can refer to virtual camera movement (defining a sequence of viewpoints), object/path-level motions (for scene elements), or synthesized camera/object interactions. The solution must satisfy:

Spatial consistency: Scenes must remain free from geometric or semantic discontinuities across the 360° canvas, especially at seams (longitude boundaries) and poles (latitude extremes).
Temporal coherence: View and object motion must be smooth, physically plausible, and consistent across frames despite wide FOV or challenging perspectives.
Trajectory adherence: The output video must accurately follow given or discovered trajectories (which may be inferred from human edits, derived from text/natural language, or based on physical law).

The technical landscape includes both the original “Pano2Vid” automatic cinematography paradigm (Su et al., 2016) and newer deep generative video synthesis models with explicit trajectory input modalities.

2. Data-Driven and Rule-Based Trajectory Selection

Pioneering work such as Pano2Vid (Su et al., 2016) frames trajectory selection as a data-driven search, where a virtual NFOV camera is steered through a 360° video. The process is as follows:

Candidate “spatio-temporal glimpses” (short NFOV clips at various directions) are extracted via dense angular sampling.
Each glimpse is scored for “capture-worthiness” by a classifier trained on web NFOV data, with 3D CNN features (C3D) and logistic regression, distinguishing positive (human-filmed) from negative (algorithmically extracted) clips.
Camera motion is constrained by smoothness: allowable changes per time-step in latitude and longitude (|ΔΩ|θ, |ΔΩ|φ ≤ ε).
Dynamic programming is used to find the optimal camera trajectory maximizing cumulative capture-worthiness under smoothness constraints, cast as a shortest path problem with syntactically valid spatio-temporal edges.

This framework is evaluated by how closely the generated camera path aligns with human-edited reference trajectories (cosine similarity, overlap metrics), and by feature-space proximity to real NFOV video (distinguishability, HumanCam-likeness, transferability).

3. Deep Generative and Diffusion-Based Trajectory Conditioning

Contemporary frameworks encode explicit spatio-temporal control into video diffusion models through several variants:

Hierarchical Latent Trajectory Encoding

Tora (Zhang et al., 31 Jul 2024): Arbitrary input trajectories are converted into dense motion maps, visualized as RGB optical flow fields, and compressed by a 3D VAE to low-dimensional hierarchical spacetime “motion patches.” These motion patches are matched in spatial/temporal shape to video patches and injected into the diffusion transformer via adaptive normalization blocks (h_i = γi * h{i−1} + βi + h{i−1}). This enables trajectory-consistent motion control over high-resolution, long-duration generation.

DragEntity (Wan et al., 14 Oct 2024) and InTraGen (Liu et al., 25 Nov 2024) each introduce conditioning pipelines wherein object/entity locations and trajectories are embedded in the latent space (via segmentation/entity masks for DragEntity; via object ID maps and sparse pose encodings for InTraGen). Multiple entities can follow different, possibly intersecting, or coordinated trajectories while preserving spatial relationships, using relational attention modules and multi-trajectory feature fusion.

Progressive Control Granularity

MagicMotion (Li et al., 20 Mar 2025): Control is progressively relaxed from dense masks to bounding boxes to sparse boxes, training the model first for detailed spatial control and then for coarse, sparse trajectory cues, leveraging a ControlNet-inspired set of convolutional layers that condition DiT blocks on injected trajectory features.

Unified Latent Trajectory Injection

ATI (Any Trajectory Instruction) (Wang et al., 28 May 2025): Users specify an arbitrary trajectory for any point/keypoint (camera or object). A Gaussian kernel injects the local trajectory signal into the spatial grid of latent video features, permitting seamless control over camera, object, or localized motion within the same model, with tail dropout regularization to prevent termination artifacts.

4. Geometry-Consistent and Cross-View Trajectory Synthesis

Some systems leverage explicit geometric modeling to ensure physically plausible, temporally consistent trajectory guidance:

Sat2Vid (Li et al., 2020) and SatDreamer360 (Ze et al., 31 May 2025): Given a satellite/overhead image and a sequence of camera poses, first build a point cloud (Sat2Vid) or compact neural tri-plane (SatDreamer360) encoding 3D structure. A virtual camera then “travels” the trajectory, using projective geometry (P = K [R|t] X; ψ, θ formulas) to render each panoramic frame, while epipolar-constrained temporal attention (in SatDreamer360) preserves frame-to-frame correspondence.
DreamJourney (Pan et al., 21 Jun 2025): A single starter image is lifted to a 3D point cloud; partial renderings (masking unseen regions) are inpainted via a video diffusion model, which reconstructs missing views guided by the specified camera trajectory. A separate stage uses an MLLM to generate prompts for dynamic object animation.

5. Panoramic Representation, Seamless Fusion, and Trajectory Alignment

Modern panoramic video generation models address geometric and continuity challenges in high-FOV outputs while integrating trajectory signals:

DynamicScaler (Liu et al., 15 Dec 2024), SphereDiff (Park et al., 19 Apr 2025), and PanoWan (Xia et al., 28 May 2025): Introduce seamless window-based or circular (ring) denoising strategies, spherical latent representations, latitude-aware noise sampling, and semantic “roll” denoising to ensure uniform coverage, seam-free transitions, and avoidance of ERP (equirectangular projection) distortion. PanoWan uses:
- Latitude-aware sampling: P'(x, y) = Interpₚ( R + (x – R) * cos((2y + 1 – R)π/(2R)), y )
- Rotated semantic denoising: cyclically rotate latents at each denoising step to distribute seam errors.
- Seamless padding in final pixel-wise decoding to eliminate edge artifacts.
ViewPoint (Fang et al., 30 Jun 2025): Proposes the ViewPoint map (a stitched pseudo-perspective set of panels from CP/ERP) and Pano-Perspective attention, recombining pretrained perspective video priors with global panoramic consistency.
VideoPanda (Xie et al., 15 Apr 2025): Employs multi-view attention layers and autoregressive windows, synchronizing synthesized perspectives over time so that trajectory-guided expansion (from text or a single view) is consistent across the panorama.
Matrix-3D (Yang et al., 11 Aug 2025): Trains a LoRA-adapted video diffusion model on mesh-rendered panoramic camera trajectories. Mesh rendering for each frame (from depth and occlusion-aware mesh) provides precise per-frame guidance during denoising, enabling wide-coverage 3D world generation.

6. Evaluation Metrics and Datasets

Leading works utilize a comprehensive battery of quantitative and qualitative metrics for measuring both video fidelity and trajectory adherence, including:

Metric	Meaning	Representative Papers
FID/FVD	Image/video quality and realism	(Wan et al., 14 Oct 2024, Li et al., 20 Mar 2025)
Trajectory Error (TrajError, ObjMC, MTEM)	Distance between predicted/generated and input trajectory across frames	(Zhang et al., 31 Jul 2024, Liu et al., 25 Nov 2024, Ji et al., 20 Mar 2025)
Mean Cosine Similarity/Overlap	Alignment of predicted virtual camera with human or ground-truth camera path	(Su et al., 2016)
CLIP-Score, Q-Align	Prompt-adherence (text-video alignment)	(Zhang et al., 2 Apr 2025 Xia et al., 28 May 2025, Luo et al., 10 Apr 2025)
Subject Consistency, Motion Smoothness	Preservation of object identity and frame-to-frame coherence	(Fang et al., 30 Jun 2025, Liu et al., 15 Dec 2024)

Datasets range from human-edited camera trajectories for real 360° videos (Pano2Vid, PanoVid), synthetic environments with depth and mesh for panoramic reconstruction (Matrix-Pano), object-focused control (MagicData, MagicBench), to large-scale cross-view collections pairing aerial with ground video (VIGOR++).

7. Applications, Implications, and Limitations

Trajectory-guided panoramic video generation has wide impacts:

Automated Cinematography: Relieves videographers/viewers from real-time viewpoint decisions, enabling story-driven, informative NFOV outputs from 360° captures (Su et al., 2016).
AR/VR and Spatial Intelligence: Seamless panoramic content with trajectory guidance is directly applicable to immersive VR, spatial analytics, simulation, world modeling, and digital twins (Liu et al., 15 Dec 2024, Yang et al., 11 Aug 2025).
Interactive Generation and Editing: Frameworks like DragEntity and ATI support user-friendly interaction (“drag-anything” or keypoint trajectories) for scene-level video design.
Physics-Grounded Forecasting: Integrating symbolic regression with video synthesis enables physically consistent trajectory guidance even in generative scenarios (Feng et al., 9 Jul 2025).
Scene Reconstruction and Exploration: Generating explorable worlds from image/text inputs facilitates applications in virtual tours, game development, and 3D content creation (Yang et al., 11 Aug 2025, Zhang et al., 2 Apr 2025).

Current limitations include challenges in adherence at extreme viewpoints or rapid motion, dependence on high-quality annotations for precise control, scalability to infinite-length or high-resolution videos (partly addressed by architectural solutions in DynamicScaler and related works), and maintaining both local and global consistency under diverse trajectory inputs.

Trajectory-guided panoramic video generation has evolved from data-driven viewpoint selection rooted in cinematographic principles to deep conditional video synthesis incorporating sophisticated spatial representations, explicit geometry, and unified trajectory interfaces. This progression has underpinned substantial advances in fidelity, controllability, and general applicability, with ongoing development focused on improving interaction paradigms, generalization to diverse real-world settings, and support for long-form, explorable, and physically-grounded panoramic video content.