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Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction (2410.18962v1)

Published 24 Oct 2024 in cs.CV

Abstract: Spatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time. Recent advancements in large-scale auto-regressive models have demonstrated remarkable capabilities across various reasoning tasks. However, these models often struggle with fundamental aspects of spatial reasoning, particularly in answering questions like "Where am I?" and "What will I see?". While some attempts have been done, existing approaches typically treat them as separate tasks, failing to capture their interconnected nature. In this paper, we present Generative Spatial Transformer (GST), a novel auto-regressive framework that jointly addresses spatial localization and view prediction. Our model simultaneously estimates the camera pose from a single image and predicts the view from a new camera pose, effectively bridging the gap between spatial awareness and visual prediction. The proposed innovative camera tokenization method enables the model to learn the joint distribution of 2D projections and their corresponding spatial perspectives in an auto-regressive manner. This unified training paradigm demonstrates that joint optimization of pose estimation and novel view synthesis leads to improved performance in both tasks, for the first time, highlighting the inherent relationship between spatial awareness and visual prediction.

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Citations (1)
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Summary

  • The paper introduces GST, a unified auto-regressive model that integrates spatial localization and view prediction through joint tokenization of images and camera poses.
  • The framework employs innovative camera tokenization and leverages VQGAN along with auto-regressive techniques to model 2D projections and spatial perspectives.
  • Experimental evaluations on datasets like CO3D and RealEstate10k show GST's superior performance in novel view synthesis and pose estimation compared to methods like Zero-1-to-3.

An Auto-Regressive Framework for Spatial Localization and View Prediction

The presented paper introduces the Generative Spatial Transformer (GST), an auto-regressive model designed to tackle spatial localization and view prediction jointly. The framework emphasizes the interconnectedness of these tasks, which are typically treated independently in existing approaches. GST unifies the representation of images and camera poses, leveraging the power of auto-regressive models to address the fundamental questions of "Where am I?" and "What will I see?"

Methodology and Innovations

At the core of the proposed approach is the novel concept of camera tokenization, which facilitates the learning of joint distributions of 2D projections and their corresponding spatial perspectives. This involves the transformation of the camera into a structured camera map using Plücker coordinates, enabling a unified tokenization method similar to that applied to images. The auto-regressive training model then operates on these token sequences to model their joint distributions.

The methodology integrates several advanced techniques, such as the use of VQGAN for image tokenization and the adaptation of the auto-regressive paradigm found in LLMs. By employing a consistent tokenization approach, GST successfully amalgamates image and camera modalities, providing a seamless framework for view synthesis and pose estimation.

Experimental Evaluation

The model is evaluated on diverse datasets, including Objaverse, CO3D, RealEstate10k, and MVImgNet. The results indicate significant improvements in both novel view synthesis and camera pose estimation tasks. GST achieves competitive performance metrics, notably surpassing methods like Zero-1-to-3 on LPIPS and SSIM scores, showcasing enhanced spatial understanding and visual prediction capabilities. The introduction of token-wise camera conditions enables the model to generate more accurate images for specific viewpoints.

Implications and Future Directions

The unification of spatial tasks in a single model framework like GST has noteworthy implications for advancing spatial intelligence in AI systems. The capability to simultaneously perform view synthesis and pose estimation enhances the model's applicability across various domains, such as robotics, augmented reality, and autonomous systems. This integration also opens avenues for future research, particularly in exploring further applications of joint distributions in spatial reasoning tasks.

One notable aspect is the model's capacity to generate valid camera poses autonomously, reflecting an understanding of spatial layout from initial observations. While the current work addresses a fundamental scenario with single observation inputs, the potential for scaling to more complex scenarios involving multiple images remains an exciting direction for further exploration.

Conclusion

The Generative Spatial Transformer stands as a significant contribution to the field of spatial reasoning in AI, offering a robust framework for bridging the gap between spatial localization and view prediction. By modeling joint distributions and employing innovative tokenization strategies, GST provides a comprehensive approach that aligns closer with human-like spatial cognition. Future work can expand its scope and refine its integration within broader AI systems.

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