[Paper] StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors

Source: arXiv - 2512.16915v1

Overview

StereoPilot tackles a growing bottleneck in the creation of 3‑D content: turning ordinary 2‑D video into high‑quality stereoscopic footage. By introducing a unified, large‑scale dataset (UniStereo) and a feed‑forward neural model that bypasses the cumbersome “depth‑warp‑inpaint” pipeline, the authors deliver a solution that is both faster and more reliable for the VR, AR, and 3‑D cinema pipelines.

Key Contributions

• UniStereo dataset – the first massive, format‑agnostic collection of paired mono‑stereo video clips covering both parallel‑view and converged‑view configurations, enabling fair benchmarking across methods.
• StereoPilot model – a single‑pass generative network that directly predicts the target eye view without explicit depth estimation or iterative diffusion, dramatically reducing latency.
• Learnable domain switcher – a lightweight module that automatically adapts the same backbone to different stereo formats (parallel vs. converged) during inference.
• Cycle‑consistency training – a novel loss that enforces consistency between the generated left/right views and the original mono frame, improving temporal stability and reducing artifacts.
• State‑of‑the‑art performance – empirical results show StereoPilot outperforms existing depth‑based and diffusion‑based approaches in visual quality while being up to 10× faster.

Methodology

1. Dataset Construction (UniStereo)

   • Collected thousands of high‑resolution video clips from existing 3‑D movies, VR captures, and synthetic sources.
   • For each clip, both parallel‑view (two cameras side‑by‑side) and converged‑view (toed‑in) stereo pairs were generated, providing a unified benchmark across formats.

2. Model Architecture

   • Backbone: A transformer‑style encoder‑decoder that ingests a single monocular frame and learns a latent representation of scene geometry and texture.
   • Domain Switcher: A small, trainable gating network that modulates the decoder’s weights based on a one‑hot stereo‑format flag, allowing the same backbone to produce either parallel or converged outputs.
   • Output Head: Directly predicts the right‑eye image (or left, depending on the flag) in a single forward pass; no depth map is produced or used.

3. Training Objectives

   • Reconstruction loss (L1 + perceptual) on the generated stereo view.
   • Cycle‑consistency loss: the generated view is re‑projected back to the original mono frame using a differentiable warping operator, encouraging geometric plausibility.
   • Adversarial loss (optional) to sharpen fine details.

4. Inference

   • Given a mono frame and a desired stereo format flag, the model instantly outputs the companion view, ready for real‑time stitching with the original frame.

Results & Findings

• Visual fidelity: StereoPilot preserves fine textures (hair, foliage) and reduces ghosting around depth edges, a common failure mode of DWI pipelines.
• Speed: The feed‑forward design eliminates the iterative diffusion steps, making it suitable for real‑time applications (≈22 fps on a single RTX 4090).
• Format robustness: The same model achieves comparable scores on both parallel and converged stereo, confirming the effectiveness of the domain switcher.

Practical Implications

• VR/AR content pipelines: Studios can now generate stereoscopic previews on‑the‑fly, cutting down the time and cost of manual dual‑camera shoots.
• Live broadcasting: Real‑time mono‑to‑stereo conversion enables 3‑D live streams for sports or concerts without dedicated 3‑D rigs.
• Game engines & simulation: Developers can integrate StereoPilot as a post‑process effect to provide optional 3‑D modes for existing 2‑D assets, expanding accessibility for headset users.
• Edge deployment: The lightweight inference (≈45 ms per frame) fits on high‑end mobile GPUs, opening possibilities for on‑device 3‑D video creation on smartphones and AR glasses.

Limitations & Future Work

• Depth ambiguity: While the model sidesteps explicit depth maps, it can still struggle with extreme parallax or transparent surfaces where geometry is inherently ambiguous.
• Training data bias: UniStereo, despite its size, is dominated by professionally shot footage; performance on low‑light or highly compressed user‑generated videos may degrade.
• Temporal consistency: Although the cycle loss reduces flicker, long‑range temporal coherence (e.g., across minutes of video) remains an open challenge.
• Future directions suggested by the authors include: incorporating self‑supervised depth cues to further improve geometry, extending the dataset with more diverse capture conditions, and exploring multi‑frame recurrent architectures for smoother video output.

Authors

• Guibao Shen
• Yihua Du
• Wenhang Ge
• Jing He
• Chirui Chang
• Donghao Zhou
• Zhen Yang
• Luozhou Wang
• Xin Tao
• Ying‑Cong Chen

Paper Information

• arXiv ID: 2512.16915v1
• Categories: cs.CV
• Published: December 18, 2025
• PDF: Download PDF