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Inverse tensor transfer for novel view synthesis [PDF]
IEEE International Conference on Image Processing 2005, 2005 This paper provides a new transfer based novel view synthesis method. This method does not need a pre-computed dense depth map, therefore overcomes most common problems associated with conventional dense correspondence algorithms, yet still produce very photo-realistic novel images.
Hongdong Li, Richard I. Hartley
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Novel-View Synthesis of Human Tourist Photos [PDF]
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 We present a novel framework for performing novel-view synthesis on human tourist photos. Given a tourist photo from a known scene, we reconstruct the photo in 3D space through modeling the human and the background independently.
Jonathan Freer +4 more
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Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis [PDF]
Neural Information Processing SystemsGeneralizable 3D Gaussian splitting (3DGS) can reconstruct new scenes from sparse-view observations in a feed-forward inference manner, eliminating the need for scene-specific retraining required in conventional 3DGS.
Zhiyuan Min +3 more
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Generative Novel View Synthesis with 3D-Aware Diffusion Models
IEEE International Conference on Computer Vision, 2023We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our model samples from the distribution of possible renderings consistent with the input and, even in the presence of ambiguity, is ...
Eric Chan +9 more
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Novel view synthesis in tensor space
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2002We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from a given tensor of three views to a novel tensor of a new configuration of three views.
Shai Avidan, Amnon Shashua
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Learning Residual Color for Novel View Synthesis
IEEE Transactions on Image Processing, 2022Scene Representation Networks (SRN) have been proven as a powerful tool for novel view synthesis in recent works. They learn a mapping function from the world coordinates of spatial points to radiance color and the scene's density using a fully connected network. However, scene texture contains complex high-frequency details in practice that is hard to
Lei Han +4 more
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ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
IEEE Transactions on Pattern Analysis and Machine IntelligenceDespite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose ViewCrafter, a novel method for synthesizing high-fidelity novel views from single or ...
Wangbo Yu +9 more
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View Independent Generative Adversarial Network for Novel View Synthesis
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019Synthesizing novel views from a 2D image requires to infer 3D structure and project it back to 2D from a new viewpoint. In this paper, we propose an encoder-decoder based generative adversarial network VI-GAN to tackle this problem. Our method is to let the network, after seeing many images of objects belonging to the same category in different views ...
Xiaogang Xu 0002 +2 more
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Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
European Conference on Computer VisionAccurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision. Current dynamic novel view synthesis methods typically require videos from many different camera viewpoints ...
Basile Van Hoorick +8 more
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Forward Flow for Novel View Synthesis of Dynamic Scenes
IEEE International Conference on Computer Vision, 2023This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping. Existing methods often adopt a static NeRF to represent the canonical space, and render dynamic images at other time steps by ...
Xiang Guo +8 more
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