StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos (2409.07447v1)

Abstract: This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience. Leveraging foundation models as priors, our approach overcomes the limitations of traditional methods and boosts the performance to ensure the high-fidelity generation required by the display devices. The proposed system consists of two main steps: depth-based video splatting for warping and extracting occlusion mask, and stereo video inpainting. We utilize pre-trained stable video diffusion as the backbone and introduce a fine-tuning protocol for the stereo video inpainting task. To handle input video with varying lengths and resolutions, we explore auto-regressive strategies and tiled processing. Finally, a sophisticated data processing pipeline has been developed to reconstruct a large-scale and high-quality dataset to support our training. Our framework demonstrates significant improvements in 2D-to-3D video conversion, offering a practical solution for creating immersive content for 3D devices like Apple Vision Pro and 3D displays. In summary, this work contributes to the field by presenting an effective method for generating high-quality stereoscopic videos from monocular input, potentially transforming how we experience digital media.

• The paper introduces a novel diffusion-based framework that converts 2D videos to high-fidelity stereoscopic 3D using advanced depth estimation and occlusion-aware inpainting.
• It employs GPU-optimized depth estimation, forward video splatting, and an auto-regressive stereo inpainting process to handle diverse video lengths and real-time processing.
• Experimental results show superior spatial consistency and temporal clarity compared to existing methods, highlighting its potential for enhancing VR/AR media experiences.

Overview of "StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos"

The paper "StereoCrafter" presents a novel approach for converting two-dimensional (2D) videos into high-fidelity stereoscopic three-dimensional (3D) content using diffusion-based techniques. Recognizing the growing demand for immersive digital experiences facilitated by advancements in VR and AR technologies, the authors propose a framework that builds upon the capabilities of foundational models to achieve high-quality stereoscopic video conversion.

Methodological Advancements

StereoCrafter's methodology integrates several cutting-edge processes to facilitate the 2D-to-3D conversion:

1. Depth-based Video Splatting and Occlusion Mask Estimation: The initial phase involves estimating depth from 2D videos using state-of-the-art techniques such as DepthCrafter and Depth Anything V2, which leverage diffusion model priors. The estimated depth maps are then utilized in a novel forward splatting method that efficiently warps frames and generates occlusion masks. The authors have optimized this method for modern GPU architectures, allowing real-time processing.
2. Stereo Video Inpainting: A customized diffusion model pre-trained on video datasets is further fine-tuned for the stereo inpainting task, addressing occlusions effectively. The model employs an auto-regressive strategy and tiled diffusion process to maintain spatial consistency and handle varying video lengths and resolutions.
3. Dataset Construction: The authors have established a comprehensive dataset by collecting stereo videos and leveraging stereo matching techniques to approximate ground-truth data. This dataset forms the backbone for training the inpainting model and ensuring high-quality stereo output.

Experimental Evaluation

StereoCrafter has been benchmarked against existing 2D-to-3D conversion methodologies like Deep3D, Owl3D, and Immersity AI. Results indicate that StereoCrafter provides sharper, more consistent stereoscopic results, addressing spatial discrepancies observed in other methodologies. Furthermore, the video inpainting capabilities of StereoCrafter outperform earlier models such as FuseFormer, E2FGVI, and ProPainter, delivering clearer and temporally consistent content in occluded areas.

Implications and Future Directions

The practical and theoretical implications of this research are profound. Practically, this method offers a scalable solution for converting existing 2D media into immersive 3D content, potentially transforming content availability for 3D display systems such as the Apple Vision Pro. Theoretically, the integration of diffusion models hints at a broader application potential within computer vision tasks requiring high-fidelity texture estimation and video synthesis.

Future research could explore further refinement of depth estimation techniques to enhance performance in complex scenes featuring dynamic movements or adverse visual conditions such as fog. Another avenue of exploration could be real-time conversion optimizations, which would significantly benefit live streaming applications. Furthermore, adapting these techniques to integrate seamlessly with existing VR/AR pipeline tools could enhance usability and integration in consumer and professional environments.

In conclusion, "StereoCrafter" significantly contributes to the 2D-to-3D video conversion field by offering a robust and effective method for creating high-quality stereoscopic videos from monocular inputs, advancing the media landscape towards a more immersive future.