Lior Yariv
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View article: Mosaic-SDF for 3D Generative Models
Mosaic-SDF for 3D Generative Models Open
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design cho…
View article: BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis Open
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-b…
View article: VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids Open
Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs). Despite their success, INRs often introduce hard to control inductive bias (i.e., the solution surface can exhibit unexpla…
View article: BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis Open
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-b…
View article: MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation Open
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge,…
View article: Volume Rendering of Neural Implicit Surfaces
Volume Rendering of Neural Implicit Surfaces Open
Neural volume rendering became increasingly popular recently due to its success in synthesizing novel views of a scene from a sparse set of input images. So far, the geometry learned by neural volume rendering techniques was modeled using …
View article: Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Open
In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates …
View article: Multiview Neural Surface Reconstruction with Implicit Lighting and Material
Multiview Neural Surface Reconstruction with Implicit Lighting and Material Open
In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates …
View article: Implicit Geometric Regularization for Learning Shapes
Implicit Geometric Regularization for Learning Shapes Open
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were computed using either: (i) pre-computed implicit shape repr…
View article: Controlling Neural Level Sets
Controlling Neural Level Sets Open
The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets c…
View article: Controlling Neural Level Sets
Controlling Neural Level Sets Open
The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surfaces. Thus, methods for controlling the neural level sets c…