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Hierarchically Structured Neural Bones for Reconstructing Animatable Objects from Casual Videos

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation model, which captures motions of objects with a tree-structured bones. Our hierarchy system decomposes motions based on the granularity and reveals the correlations between parts without exploiting any prior structural knowledge. We further propose to regularize the bones to be positioned at the basis of motions, centers of parts, sufficiently covering related surfaces of the part. This is achieved by our bone occupancy function, which identifies whether a given 3D point is placed within the bone. Coupling the proposed components, our framework offers several clear advantages: (1) Users can obtain animatable 3D models of the arbitrary objects in improved quality from their casual videos, (2) users can manipulate 3D models in an intuitive manner with minimal costs, and (3) users can interactively add or delete control points as necessary. The experimental results demonstrate the efficacy of our framework on diverse instances, in reconstruction quality, interpretability and easier manipulation. Our code is available at https://github.com/subin6/HSNB.

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Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2022-0-00124), the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (NRF- 2022R1A2C2004509), and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (Artificial Intelligence Graduate School Program, Yonsei University, under Grant 2020-0-01361).

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Authors and Affiliations

  1. Yonsei University, Seoul, South Korea

    Subin Jeon, In Cho, Minsu Kim, Woong Oh Cho & Seon Joo Kim

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  1. Subin Jeon
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  2. In Cho
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  3. Minsu Kim
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  4. Woong Oh Cho
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Correspondence to Subin Jeon .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Jeon, S., Cho, I., Kim, M., Cho, W.O., Kim, S.J. (2025). Hierarchically Structured Neural Bones for Reconstructing Animatable Objects from Casual Videos. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15068. Springer, Cham. https://doi.org/10.1007/978-3-031-72684-2_23

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