Wonderland: Navigating 3D Scenes from a Single Image (2412.12091v2)

Abstract: How can one efficiently generate high-quality, wide-scope 3D scenes from arbitrary single images? Existing methods suffer several drawbacks, such as requiring multi-view data, time-consuming per-scene optimization, distorted geometry in occluded areas, and low visual quality in backgrounds. Our novel 3D scene reconstruction pipeline overcomes these limitations to tackle the aforesaid challenge. Specifically, we introduce a large-scale reconstruction model that leverages latents from a video diffusion model to predict 3D Gaussian Splattings of scenes in a feed-forward manner. The video diffusion model is designed to create videos precisely following specified camera trajectories, allowing it to generate compressed video latents that encode multi-view information while maintaining 3D consistency. We train the 3D reconstruction model to operate on the video latent space with a progressive learning strategy, enabling the efficient generation of high-quality, wide-scope, and generic 3D scenes. Extensive evaluations across various datasets affirm that our model significantly outperforms existing single-view 3D scene generation methods, especially with out-of-domain images. Thus, we demonstrate for the first time that a 3D reconstruction model can effectively be built upon the latent space of a diffusion model in order to realize efficient 3D scene generation.

• The paper demonstrates a new pipeline that leverages latent spaces from a video diffusion model to reconstruct coherent 3D scenes from a single image.
• It introduces a camera-guided dual-branch strategy with 3D Gaussian Splatting for efficient, feed-forward scene generation that avoids iterative optimization.
• Quantitative tests on datasets like RealEstate10K and Tanks-and-Temples reveal significant improvements in pose control and visual fidelity over previous methodologies.

Overview of "Wonderland: Navigating 3D Scenes from a Single Image"

The paper "Wonderland: Navigating 3D Scenes from a Single Image" proposes an innovative method for constructing expansive and coherent 3D scenes from a single image input. This approach tackles several limitations present in existing methods, most notably the dependencies on multi-view data and significant computational demand for per-scene optimization. The core contribution is a novel pipeline that leverages latent spaces derived from a video diffusion model, allowing for efficient and high-quality 3D scene reconstruction.

The authors introduce a camera-guided video diffusion model that captures scenes with specific camera trajectories and multi-view information while maintaining 3D consistency. This model predicts 3D Gaussian Splattings in a feed-forward manner, enabling comprehensive scene generation from minimal input data. The utilization of a diffusion model in this context is significant for its compression capabilities and inherent 3D awareness, which contribute to remarkable results in generating coherent and detailed visual outputs from a single image.

Key Contributions and Results

1. Innovative Use of Video Diffusion Models: The paper highlights the integration of video diffusion models, configured with dual-branch camera conditioning mechanisms for precise control of camera trajectories. This design allows the model to handle spatial relationships effectively across multiple views, a challenge for earlier diffusion and NeRF models.
2. Efficient 3D Scene Representation via Gaussian Splatting: By using 3D Gaussian Splattings (3DGS), the authors efficiently represent and render large and detailed 3D scenes. This approach diversifies and improves in terms of quality and scope when compared to previous techniques that required dense multi-view training data.
3. Quantitative Superiority: The model has undergone rigorous testing across varied datasets, with results showing substantial performance enhancements over current single-image to 3D generation methodologies. Metrics across benchmarks like RealEstate10K, DL3DV, and Tanks-and-Temples evidenced higher precision in pose control and improved visual fidelity.
4. Feed-Forward 3D Reconstruction: The system sidesteps the computational burden of iterative optimization seen in other approaches by producing 3D structures in a single forward pass. This reduction in computational overhead facilitates faster and more practical applications of 3D scene generation.

Implications and Future Directions

The practical implications of this paper span industries ranging from virtual reality applications and gaming to architectural visualization and autonomous vehicles, where robust and quick 3D scene understanding is crucial. The theoretical implications suggest promising future directions in video diffusion models, particularly their application in multidimensional data representation and synthesis.

Future work could explore extending this approach to dynamic scenes or improving the speed and efficiency of the diffusion model to support real-time applications. Additionally, further research could investigate integrating temporal dimensions into scene generation, enhancing applicability in scenarios requiring 4D scene synthesis, thus allowing for dynamic interactions within virtual environments.

In conclusion, the "Wonderland" paper presents a robust framework for deriving comprehensive 3D scenes from a single image, substantially advancing the state-of-the-art in single-view scene generation. Its contributions in integrating video diffusion models to achieve consistent, large-scope, and high-quality 3D visualizations highlight its potential for significant impact across multiple fields.