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Computer Science > Computer Vision and Pattern Recognition

arXiv:2204.10850 (cs)
[Submitted on 22 Apr 2022]

Title:Control-NeRF: Editable Feature Volumes for Scene Rendering and
Manipulation

Authors:Verica Lazova, Vladimir Guzov, Kyle Olszewski, Sergey
Tulyakov, Gerard Pons-Moll
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    Abstract: We present a novel method for performing flexible,
    3D-aware image content manipulation while enabling high-quality
    novel view synthesis. While NeRF-based approaches are effective
    for novel view synthesis, such models memorize the radiance for
    every point in a scene within a neural network. Since these
    models are scene-specific and lack a 3D scene representation,
    classical editing such as shape manipulation, or combining scenes
    is not possible. Hence, editing and combining NeRF-based scenes
    has not been demonstrated. With the aim of obtaining
    interpretable and controllable scene representations, our model
    couples learnt scene-specific feature volumes with a scene
    agnostic neural rendering network. With this hybrid
    representation, we decouple neural rendering from scene-specific
    geometry and appearance. We can generalize to novel scenes by
    optimizing only the scene-specific 3D feature representation,
    while keeping the parameters of the rendering network fixed. The
    rendering function learnt during the initial training stage can
    thus be easily applied to new scenes, making our approach more
    flexible. More importantly, since the feature volumes are
    independent of the rendering model, we can manipulate and combine
    scenes by editing their corresponding feature volumes. The edited
    volume can then be plugged into the rendering model to synthesize
    high-quality novel views. We demonstrate various scene
    manipulations, including mixing scenes, deforming objects and
    inserting objects into scenes, while still producing
    photo-realistic results.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2204.10850 [cs.CV]
          (or arXiv:2204.10850v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2204.10850
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kyle Olszewski [view email]
[v1] Fri, 22 Apr 2022 17:57:00 UTC (29,181 KB)
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