StereoPilot Model Architecture

• StereoPilot is a unified neural model that directly synthesizes stereo views without explicit depth estimation, eliminating error propagation from traditional DWI pipelines.
• It integrates a learnable domain switcher and cycle consistency loss to handle both parallel and converged stereo formats, ensuring geometric and photometric consistency.
• Benchmarking on the UniStereo dataset shows significant gains in visual fidelity and efficiency, reducing inference latency to approximately 11 seconds per clip.

StereoPilot is an efficient, unified, feed-forward neural architecture designed for automatic monocular-to-stereo video conversion across both parallel and converged stereo formats. Developed to address key limitations of the prevailing multi-stage “Depth-Warp-Inpaint” (DWI) pipeline—including error propagation, depth ambiguity, and format-locking—StereoPilot directly synthesizes the target stereo view without explicit depth estimation or iterative diffusion sampling. Its architecture integrates a learnable domain switcher and a cycle consistency loss to ensure robust generalization and seamless adaptation to different stereo configurations. Extensive benchmarking on the UniStereo dataset demonstrates significant improvements over existing methods, both in visual fidelity and computational efficiency (Shen et al., 18 Dec 2025).

1. Motivation and Background

Stereoscopic displays have proliferated in domains such as VR headsets and 3D cinemas, creating demand for high-quality stereo video content. Manual production of stereo videos is resource-intensive, and native 3D capture incurs substantial costs. Automated mono-to-stereo pipelines conventionally follow a sequential DWI approach: estimating per-pixel depth, warping the monocular view, and inpainting occluded regions. However, DWI is susceptible to cumulative error propagation, depth ambiguities in challenging regions (such as reflections or transparent surfaces), and rigidity with respect to stereo format—typically handling either parallel or converged offsets, but not both. These limitations motivate a unified, end-to-end approach capable of addressing format diversity and improving both output quality and efficiency (Shen et al., 18 Dec 2025).

2. Architectural Overview of StereoPilot

StereoPilot is a feed-forward model that, unlike DWI or diffusion-based architectures, bypasses explicit depth regression and warp-inpaint loops. Instead, the architecture encodes the input monocular video and synthesizes the target stereo view directly via generative priors learned from data. A learnable domain switcher is embedded within the model to explicitly condition the synthesis process on stereo format (parallel or converged). This conditional mechanism is essential for achieving format consistency since disparities, camera geometry, and target viewpoints differ fundamentally between parallel and converged stereoscopic captures.

A cycle consistency loss is incorporated, encouraging the model to reconstruct the source monocular input from its generated stereo counterpart—thereby promoting geometric and photometric consistency across views and temporal sequences. StereoPilot processes a fixed temporal context (e.g., 81 frames at 16 fps and 832×480 spatial resolution) and leverages both spatial and temporal cues in its synthesis, supporting robust conversion of dynamic video content.

3. Domain Switching and Cycle Consistency Mechanisms

The domain switcher is a learnable module that receives stereo format tags (parallel or converged) and modulates feature flows within the synthesis pipeline. This design removes the need for training separate models for each stereo protocol, facilitating seamless generalization across both formats. The switcher is essential for resolving the disparities and geometric differences inherent to parallel and converged stereo. Parallel stereo, as encoded in the Stereo4D subset, uses a fixed baseline $B \approx 6.3$ cm (VR180 standard) and a fixed horizontal field-of-view $\text{hfov} = 90^\circ$; image disparities follow $d = \frac{fB}{Z}$.

In converged geometry (3DMovie), cameras are toed-in at a rotation angle $\theta$ towards a convergence plane at depth $Z_c$, and synthesized disparities can be both positive and negative around the zero-disparity plane, typically $|d| \le 40$ pixels. The switcher conditions representations for these geometric differences, and the cycle consistency loss regularizes the mapping from monocular to stereo and back, reducing domain gap artifacts and preserving scene semantics (Shen et al., 18 Dec 2025).

4. Training Protocol and Benchmarking on UniStereo

StereoPilot is trained on the UniStereo dataset, which aggregates 108,000 stereo-caption video pairs—combining Stereo4D (parallel, VR180) and 3DMovie (converged, SBS-encoded films). The training split comprises 58,000 Stereo4D clips and 44,879 3DMovie clips, all processed to 81 frames at 16 fps and 832×480 resolution, ensuring consistent spatial-temporal structure across formats. Ground-truth is provided via full stereo video pairs rather than per-pixel depths, although scene geometry (such as camera-to-world extrinsics) is available in Stereo4D for disparity evaluation. Automated and manual quality controls are implemented to ensure dataset fidelity, including removal of pseudo-stereo instances and cropping of non-relevant content.

Evaluation uses 400 held-out test clips from each dataset subset. Metrics for benchmarking include PSNR, SSIM, MS-SSIM, LPIPS (lower is better), SIOU (higher is better), and inference latency. StereoPilot achieves SSIM scores of 0.861 (Stereo4D) and 0.837 (3DMovie), exceeding prior methods (e.g., StereoDiffusion: 0.642/0.678, StereoCrafter: 0.553/0.706, Mono2Stereo: 0.649/0.795). Inference latency is approximately 11 seconds per clip, vastly outperforming diffusion and sequential pipelines (1–60 minutes) (Shen et al., 18 Dec 2025).

Method	Stereo4D (SSIM↑)	3DMovie (SSIM↑)	Latency
StereoDiffusion	0.642	0.678	~60 min
StereoCrafter	0.553	0.706	~1 min
Mono2Stereo	0.649	0.795	~15 min
StereoPilot	0.861	0.837	~11 s

5. Stereo Geometry and Format Normalization

The architecture explicitly models and adapts to both parallel and converged stereo geometries. In parallel rigs, the left-right disparity is governed by $d = x_l - x_r = \frac{fB}{Z}$, with $f$ representing the focal length and $B$ the baseline. Depth $Z$ is then $Z = \frac{fB}{d}$. In converged (toed-in) rigs, projections are described by

$$\begin{aligned} [X_L,Y_L,Z_L]^T &= R(-\theta)\,[X+\tfrac B2,\,Y,\,Z]^T,\ x_l &= f\,\frac{X_L}{Z_L}, \ [X_R,Y_R,Z_R]^T &= R(+\theta)\,[X-\tfrac B2,\,Y,\,Z]^T,\ x_r &= f\,\frac{X_R}{Z_R} \end{aligned}$$

with $R(\cdot)$ as the camera rotation matrix. The model’s normalization and encoding modules are format-aware via the domain switcher, ensuring geometric compatibility and support for both disparity structures. This dual compatibility is a principal distinguishing factor, allowing unified model deployment and fair method benchmarking (Shen et al., 18 Dec 2025).

6. Robustness Across Scene Types and Generalization

StereoPilot is trained on a broad range of indoor and outdoor scenes, including static and dynamic camera motion, moving objects, reflective surfaces, and varied lighting conditions. 3DMovie clips introduce additional challenges from cinematic camera work (pans, cuts, depth-of-field). The absence of per-pixel depth supervision is offset by large-scale stereo pairs and cycle consistency, which together support strong temporal and spatial generalization abilities. Format tags and uniform dataset characteristics (resolution, frame count, caption schema) reduce format bias and foster domain-robust network behavior.

A plausible implication is that StereoPilot’s architecture, with its learned generative prior over stereo pairs and explicit conditioning on geometric format, can generalize to novel video domains and scenes exhibiting significant depth or geometric ambiguity.

7. Significance and Impact

StereoPilot’s unified, efficient approach addresses several central limitations in the state-of-the-art for monocular-to-stereo conversion. By eliminating intermediate depth estimation and warping stages, the model avoids the primary causes of error propagation and artifacts in existing methods. Its ability to adapt to both parallel and converged configurations within a single network is enabled by the domain-switching mechanism and loss design. Empirical results demonstrate that, on a standardized and comprehensive benchmark, StereoPilot materially advances both fidelity and computational efficiency for automated 2D-to-3D video synthesis (Shen et al., 18 Dec 2025). This suggests substantial potential for accelerating content creation workflows for emerging AR/VR and cinematic applications, as well as providing a new baseline architecture for future stereo conversion research.