Trajectory-Guided Panoramic Video Diffusion

• Trajectory-guided panoramic video diffusion models are generative systems that synthesize immersive 360° videos using explicit trajectory control for consistent motion and spatial alignment.
• They integrate latent diffusion techniques with spherical representations and customized conditioning methods to overcome distortions and maintain continuity across wide fields of view.
• The approach advances applications in VR/AR and automated video generation by adapting pretrained image diffusion methods to high-dimensional, trajectory-controlled video synthesis.

A trajectory-guided panoramic video diffusion model refers to a class of generative systems that synthesize temporally and spatially consistent 360° panoramic video sequences, where camera or object movement is explicitly guided by predefined trajectories or user-specified control signals. This paradigm generalizes controllable generation to the field of high-dimensional, immersive video, integrating geometric, photometric, and motion consistency constraints across wide fields of view.

1. Foundations of Trajectory-Guided Panoramic Video Diffusion

Trajectory-guided panoramic video diffusion models are an overview of several domains: guided diffusion modeling, panoramic neural representations, view synthesis, and control-centric generative methods. The problem formulation typically involves

• a high-dimensional stochastic process (diffusion) operating over spatiotemporal latents representing panoramic (often equirectangular, cubemap, or spherical) imagery,
• explicit or implicit motion trajectories (camera or object paths) injected as conditions or priors,
• dedicated architectural or loss-based innovations to ensure spherical continuity, consistency across wide fields of view, and fine-grained motion control.

These models draw heavily on latent diffusion methods—where a VAE compresses images or videos into a latent space for denoising, massively reducing compute (Li et al., 2023)—as well as on classical view synthesis approaches that inject camera pose or geometric cues at each step (Yu et al., 2023, Ye et al., 31 Oct 2024, Pan et al., 21 Jun 2025).

2. Model Architectures and Conditioning Strategies

The core generative process in trajectory-guided panoramic video diffusion models is a Markovian denoising chain, typically defined for latent representations $z \in \mathbb{R}^{C \times T \times H \times W}$, where $T$ is the number of frames, $H$ and $W$ are spatial resolutions, and $C$ the channel dimension. The forward process involves a noise schedule:

$$q(z_t | z_{t-1}) = \mathcal{N}(\sqrt{1-\beta_t} z_{t-1}, \beta_t I)$$

with learned reverse transition conditioned on trajectory input and (optionally) semantic context.

Trajectory Conditioning may take the form of:

• Explicit camera paths, provided as sequences of extrinsic matrices or pose vectors and embedded via Fourier or learned embeddings (Jiang et al., 25 Feb 2024, He et al., 13 Mar 2025, Kwak et al., 2023).
• Object or agent trajectories, supplied as bounding box sequences, tracklets, or target waypoints, sometimes with additional features (e.g., group membership, motion priors) (Li et al., 2023, Rempe et al., 2023).
• Optical flow fields describing expected global or local motion (Wang et al., 12 Jan 2024, Voynov et al., 2023).

Architectural approaches for conditioning include:

• Camera injection layers or trajectory-adaptive normalization (e.g., FiLM) (Zhang et al., 31 Jul 2024, He et al., 13 Mar 2025).
• Cross-attention modules, either standard or customized to handle per-frame pose information and multi-view context (Ye et al., 31 Oct 2024, Xie et al., 15 Apr 2025).
• Multi-stream U-Nets with geometry-aware feature mixing, enabling separate but interactive conditioning on the current view, previous frame, and trajectory features (Yu et al., 2023).

3. Panoramic Representation, Spherical Consistency, and Boundary Handling

A central challenge in panoramic video synthesis is maintaining geometric and photometric consistency across all directions—especially at latitude/longitude boundaries and poles, where traditional equirectangular projection (ERP) distorts sampling density and often introduces discontinuities.

Systems employ several strategies:

• Spherical Latent Embedding: SphereDiff (Park et al., 19 Apr 2025) replaces ERP grids with a uniform Fibonacci lattice on $S^2$; MultiDiffusion fusion aggregates multiple perspective latents via distortion-aware weighted averaging.
• Cube Face, ViewPoint, and Pseudo-perspective Mappings: Some models unfold the 360° view into a sequence of cube faces or overlapping Perspective panels, aligning with pretrained model priors and supporting per-panel attention/fusion (Fang et al., 30 Jun 2025).
• Latitude-Aware Sampling and Rotated Denoising: Techniques such as in PanoWan (Xia et al., 28 May 2025) resample or shift latent rows according to their angular latitude to compensate for ERP distortion, while denoising steps employ rotated latent windows or circular padding to enforce seam continuity.
• Epipolar-Aware Cross-Attention: DiffPano (Ye et al., 31 Oct 2024) introduces an epipolar-aware attention module derived for spherical geometry, enforcing spatial consistency across views sampled along a trajectory.

Addressing these representational complexities is crucial for seamless, artifact-free panoramic video.

4. Trajectory Guidance: Control, Motion Consistency, and Test-time Steering

Motion trajectory control spans camera movement, object paths, or more general scene flow:

• Test-time Guidance: Many models (e.g., TRACE/“Trace & Pace” (Rempe et al., 2023), FreeTraj (Qiu et al., 24 Jun 2024)) support on-the-fly control by constructing differentiable guidance losses (waypoint, speed, inter-agent distance, adherence to physics/plausibility, etc.) and injecting their gradients into the sampling process. Clean guidance avoids perturbing the noisy denoising process directly, instead acting on the predicted clean output at each step.
• Noise and Attention Editing: FreeTraj injects trajectory signals in initial noise low-frequency components and applies spatial/temporal attention masks to steer object movement at test time, all without further training. This enables manual or LLM-planned trajectories while maintaining image/video fidelity.
• Trajectory-aware Latent Representations: Some architectures use trajectory maps or flow images encoded by 3D VAEs and injected via FiLM-like normalization at each block, supporting complex, scalable motion guidance (Zhang et al., 31 Jul 2024).
• Scheduling Model Influence: In multi-branch models (e.g., ViVid-1-to-3 (Kwak et al., 2023)), the relative weights of view-conditioned and video diffusion branches are dynamically scheduled over sampling steps to balance pose fidelity and global spatio-temporal consistency.

Robust trajectory adherence is measured via trajectory error metrics, centroid distance, mean Intersection-over-Union (mIoU), optical flow consistency, and specialized epipolar criteria (Ye et al., 31 Oct 2024).

5. Training Paradigms, Datasets, and Evaluation

Trajectory-guided panoramic models are typically adapted from pretrained image/video diffusion models to the 360° domain via:

• Parameter-efficient adaptation (e.g., LoRA (Ye et al., 31 Oct 2024, Xia et al., 28 May 2025)) or minimal additional modules (e.g., lightweight adapters, motion extractors (Wang et al., 12 Jan 2024)), so as to retain the semantic and spatial priors learned from large perspective databases.
• Curated panoramic datasets such as WEB360 (Wang et al., 12 Jan 2024), PanoVid (Xia et al., 28 May 2025), 360World (Zhou et al., 30 Apr 2025), and large-scale synthetic/collected multi-view datasets with high-quality captions, precise camera poses, panoramic depths, and diverse scenarios.
• Data augmentation and hybrid training: Mixing perspective and panorama videos, randomizing view/temporal matrix windows (Xie et al., 15 Apr 2025), and using panoramic-specific caption fusion strategies or motion-based filtering.

Metrics include standard image/video measures (FID, FVD, IS, CLIP-Score) as well as panoramic-specific continuity, distortion, and coverage metrics, as well as task-specific metrics such as FRÉCHET Video Distance (FVD), thresholded symmetric epipolar distance (TSED), and panoramic end continuity.

6. Applications and Extensions

Trajectory-guided panoramic video diffusion models support a range of applications:

• Virtual and Augmented Reality (VR/AR): Generation of dynamic, immersive 360° content for world models, VR backdrops, and spatial intelligence platforms (Ye et al., 31 Oct 2024, Liu et al., 15 Dec 2024, Zhou et al., 30 Apr 2025).
• Interactive Dynamic Scene Exploration: CameraCtrl II (He et al., 13 Mar 2025) and DreamJourney (Pan et al., 21 Jun 2025) demonstrate controlled navigation through dynamic scenes, with iterative user-specified trajectory input.
• Cinematic and Crowd Simulation: TRACE (Rempe et al., 2023) enables simulation of pedestrian and crowd behaviors at scale, with social and environmental context.
• Automated Video Augmentation and Data Generation: TrackDiffusion (Li et al., 2023) and PoseTraj (Ji et al., 20 Mar 2025) use trajectory-guided synthetic videos to improve downstream tracking and perception models via data augmentation.
• 4D Scene Asset Construction: HoloTime (Zhou et al., 30 Apr 2025) generates spatially and temporally consistent 4D Gaussian Splatting models directly from panoramic videos.

Zero-shot downstream tasks such as panoramic super-resolution, inpainting, seamless loop generation, and dynamic outpainting are made possible via the modularity and generality of the lifted diffusion approach (Xia et al., 28 May 2025, Park et al., 19 Apr 2025).

7. Challenges and Open Directions

Several challenges persist:

• Distortion and Discontinuities: ERP-based methods remain prone to boundary artifacts; recent work on spherical latents and advanced fusion addresses but does not completely solve these issues.
• Scalability and Efficiency: Handling larger resolutions and longer video windows in the panoramic domain exposes memory, compute, and temporal coherence bottlenecks. Window-based denoising and upscaling pipelines (e.g., DynamicScaler (Liu et al., 15 Dec 2024)) enable arbitrary-size synthesis with constant VRAM but may incur patch artifacts if not carefully managed.
• Motion/Camera Decoupling: Disentangling object and camera motion, particularly for complex trajectories involving large 6D pose variations and rotations, remains non-trivial; pose-aware training and camera disentanglement modules show promise (Ji et al., 20 Mar 2025).
• Data Scarcity: While large datasets are emerging, panoramic video with dense pose, depth, and semantic captions remains less diverse and abundant than conventional datasets, potentially limiting coverage of edge cases.

Trajectory-guided panoramic video diffusion models are rapidly advancing in capability, increasingly able to synthesize dynamic, immersive, and spatially precise content under explicit trajectory control. Their methodological innovations—spanning representational, architectural, and training paradigms—provide a foundation for next-generation generative systems in immersive, interactive, and cinematic environments.