Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes (2011.13084v3)

Abstract: We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.

• The paper introduces a novel neural representation for scene flow that enables continuous space-time view synthesis of dynamic scenes.
• It integrates motion estimation with view synthesis to reconstruct detailed 3D structures and natural temporal transitions in complex environments.
• The method demonstrates success through quantitative metrics and visual results, underscoring its potential for applications in VR and video editing.

Overview of CVPR Author Guidelines in LaTeX

The provided document serves as a comprehensive guide for authors preparing their submissions for the CVPR (Conference on Computer Vision and Pattern Recognition) proceedings using LaTeX. This guide includes detailed formatting instructions to ensure uniformity across submissions. Key elements discussed range from manuscript length to formatting specifics, all developed to optimize the submission and review process.

Key Elements of the Guidelines

1. Abstract and Introduction Formatting: The abstract should appear in fully justified, italicized text below author information. A clear structure is emphasized to set the style and length in alignment with previous CVPR abstracts.
2. Submission Requirements:
   • Manuscripts are to be written in English and must adhere to a strict page limit of eight pages, not counting references.
   • Overlength papers will not be reviewed, and authors are encouraged to use proper LaTeX settings for figures and references.
3. Blind Review Process: Authors are reminded to preserve anonymity by avoiding self-references that may reveal their identity. Proper citation and the use of third-person language are recommended.
4. Mathematics and Equations: All equations should be numbered to facilitate reference. The guidelines stress the importance of clear mathematical communication.
5. Formatting Specifications: Submissions must follow a two-column format with specific measurements for text areas, margins, and spacing. Consistency in type-style, fonts, and headings is emphasized to maintain aesthetic integrity.
6. Use of Graphics: Authors should ensure their graphics are legible when printed. Guidelines for resizing and centering images using LaTeX commands are provided.
7. Illustrations and Tables: Guidelines for integrating illustrations and tables within the text, including caption formatting, are detailed to maintain clarity and coherence.
8. Final Submission Requirements: Authors are required to submit a signed IEEE copyright release form to ensure their paper can be legally published.

Implications and Future Considerations

The stringent requirements outlined in the CVPR author guidelines highlight the conference's commitment to maintaining high standards in its proceedings. This uniform approach allows for easier navigation by researchers who rely on consistent formats for efficient information retrieval.

Moreover, as technological advancements continue to influence paper submissions, there is potential for integrating more dynamic elements, such as interactive graphics and dynamic datasets. Future revisions of these guidelines may address evolving computational tools and growing interdisciplinary research areas in AI.

In summary, these author guidelines serve as a vital resource for researchers aiming to contribute to CVPR. Following these directives not only ensures a smoother review process but also enhances the communication of scientific contributions within the computer vision community.