Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image (2203.09457v1)

Abstract: Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques. However, most work is still limited by synthesizing new views within relatively small camera motions. In this paper, we propose a novel approach to synthesize a consistent long-term video given a single scene image and a trajectory of large camera motions. Our approach utilizes an autoregressive Transformer to perform sequential modeling of multiple frames, which reasons the relations between multiple frames and the corresponding cameras to predict the next frame. To facilitate learning and ensure consistency among generated frames, we introduce a locality constraint based on the input cameras to guide self-attention among a large number of patches across space and time. Our method outperforms state-of-the-art view synthesis approaches by a large margin, especially when synthesizing long-term future in indoor 3D scenes. Project page at https://xrenaa.github.io/look-outside-room/.

• The paper introduces an autoregressive Transformer that generates multiple future frames with enhanced spatial-temporal consistency.
• It leverages camera-aware locality constraints during self-attention to preserve shared scene geometry across synthesized views.
• Empirical evaluations on Matterport3D and RealEstate10K show significant improvements in LPIPS, PSNR, and overall perceptual quality.

Synthesizing Long-Term 3D Scene Videos from a Single Image: Methodology and Implications

The paper "Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image" by Xuanchi Ren and Xiaolong Wang presents a novel approach for novel view synthesis leveraging a single image. This research addresses a significant limitation in current view synthesis methodologies—namely, synthesizing images with a broad scope of camera motion while maintaining consistency over long-term trajectories. This challenge is particularly prevalent in indoor 3D scenes where structural reasoning is crucial.

Core Contributions and Methodology

The authors propose a method based on an autoregressive Transformer model that synthesizes 3D scene videos over an extended trajectory from a static input image and associated camera movements. The technique utilizes an autoregressive framework to model sequentially, thereby cultivating a spatial-temporal understanding that allows the model to generate consistent future frames.

The core contributions of the paper are as follows:

1. Autoregressive Transformer Model: The approach introduces a novel Transformer model, which performs autoregressive synthesis of 3D scenes by modeling multiple future frames together with their associated camera movements. This iterative modeling ensures perceptual and semantic consistency across generated views.
2. Camera-Aware Locality Constraints: The method incorporates camera-aware biases during self-attention operations. This aspect is critical because it facilitates the Transformer model to emphasize local dependencies influenced by camera relations, thus ensuring that shared scene geometry is preserved across frames.
3. State-of-the-Art Performance: Empirical evaluations exhibit that the proposed solution significantly surpasses existing state-of-the-art techniques in both conventional metrics (LPIPS and PSNR) and human-perceived quality, especially in long-term video synthesis tasks involving complex camera trajectories.

Experimental Evaluation

Experiments were conducted on Matterport3D and RealEstate10K datasets, predominantly focusing on 3D indoor scenes. The results demonstrate that the method not only generates synthesized images with higher perceptual consistency over long sequences but also maintains high fidelity in terms of image realism compared to geometric-based and other geometry-free baselines.

Theoretical and Practical Implications

The theoretical implications of this research are profound; it demonstrates the viability of utilizing autoregressive models for complex visual synthesis problems, transcending the limitations posed by conventional geometry-based methods. This approach potentially redefines methodologies in 3D scene synthesis by offering scalability in terms of view change and sequence length.

From a practical perspective, the presented technique opens new avenues in areas such as content creation, virtual reality environments, and autonomous robotics. The ability to synthesize realistic, consistent long-term scene renditions from singular images can greatly benefit these domains, where visual continuity and fidelity are paramount.

Future Directions

The research sets the groundwork for future advancements in visual synthesis involving more robust and scalable models. Potential developments can include enhanced inference speed for real-time applications, refined metrics for evaluating synthesized content, and exploring applications in diverse environments beyond indoor scenes.

In summary, the paper provides a compelling framework for breaking through existing constraints in novel view synthesis, offering a new lens through which to explore and implement advanced AI-driven visual technologies.