Results 21 to 30 of about 171,197 (277)
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Recent work has demonstrated that volumetric scene representations combined with differentiable volume rendering can enable photo-realistic rendering for challenging scenes that mesh reconstruction fails on. However, these methods entangle geometry and appearance in a "black-box" volume that cannot be edited.
Xiang, Fanbo +5 more
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, 2020 We propose a framework for learning neural scene representations directly from images, without 3D supervision. Our key insight is that 3D structure can be imposed by ensuring that the learned representation transforms like a real 3D scene. Specifically, we introduce a loss which enforces equivariance of the scene representation with respect to 3D ...
Dupont, Emilien +6 more
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MonoNHR: Monocular Neural Human Renderer
2022 International Conference on 3D Vision (3DV), 2022 Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas. In this regard, we propose Monocular Neural Human Renderer (MonoNHR), a novel approach that renders robust free-viewpoint images of an arbitrary human given only a single image.
Choi, Hongsuk +5 more
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Invertible Neural BRDF for Object Inverse Rendering [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry. The BRDF is expressed with an invertible neural network, namely, normalizing flow, which provides the expressive power of a high ...
Zhe Chen, Shohei Nobuhara, Ko Nishino
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Pulsar: Efficient Sphere-based Neural Rendering [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 We propose Pulsar, an efficient sphere-based differentiable renderer that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from ...
Lassner, Christoph, Zollhöfer, Michael
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Extending stochastic resonance for neuron models to general Levy noise [PDF]
, 2009 A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show
Applebaum, D.
core +1 more source
Generalizable Patch-Based Neural Rendering
, 2022 Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The recent focus has been on models that overfit to a single scene, and the few attempts to learn models that can synthesize novel views of unseen scenes mostly consist of ...
Suhail, Mohammed +3 more
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Automated pebble mosaic stylization of images [PDF]
, 2019 Digital mosaics have usually used regular tiles, simulating the historical "tessellated" mosaics. In this paper, we present a method for synthesizing pebble mosaics, a historical mosaic style in which the tiles are rounded pebbles.
Anderson, Forest +3 more
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Collaborative Neural Rendering Using Anime Character Sheets
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). In general,
Lin, Zuzeng, Huang, Ailin, Huang, Zhewei
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