Learning Implicit Fields For Generative Shape Modeling Learning

By themelower On Apr 26, 2026

Learning Implicit Fields For Generative Shape Modeling We advocate the use of implicit fields for learning gen erative models of shapes and introduce an implicit field de coder, called im net, for shape generation, aimed at im proving the visual quality of the generated shapes. We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called im net, for shape generation, ai.

Learning Implicit Fields For Generative Shape Modeling In this paper, we explore the use of implicit fields for learning deep models of shapes and introduce an implicit field decoder for shape generation, aimed at improving the visual quality of the generated models, as shown in fig ure 1. Trained one model on the 13 shapenet categories as most single view reconstruction networks do. for each category, sort the object names and use the first 80% as training set, the rest as testing set, same as atlasnet. This allows assessing the capabilities of our novel de coder for tasks such as shape representation learning, 2d or 3d shape generation, shape interpolation, as well as single view 3d shape reconstruction. By replacing conventional decoders by the implicit decoder for representation learning and shape generation, this work demonstrates superior results for tasks such as generative shape modeling, interpolation, and single view 3d reconstruction, particularly in terms of visual quality.

Learning Implicit Fields For Generative Shape Modeling This allows assessing the capabilities of our novel de coder for tasks such as shape representation learning, 2d or 3d shape generation, shape interpolation, as well as single view 3d shape reconstruction. By replacing conventional decoders by the implicit decoder for representation learning and shape generation, this work demonstrates superior results for tasks such as generative shape modeling, interpolation, and single view 3d reconstruction, particularly in terms of visual quality. We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder for shape generation, aimed at improving the visual. Scope of work our implicit field decoder, im net, can be embedded into different shape analysis and synthesis frameworks to support various applications. for our project, we demonstrate auto encoding and generation of 3d objects or shape generation. Abstract: we advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called im net, for shape generation, aimed at improving the visual quality of the generated shapes.

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Learning Structured Implicit Shape Representations - Part 1/4

Learning Structured Implicit Shape Representations - Part 1/4

Learning Structured Implicit Shape Representations - Part 1/4 DeepCurrents: Learning Implicit Representations of Shapes with Boundaries (CVPR 2022) Lec 15. Generative Models: Representation Learning Meets Generative Modeling TUM AI Lecture Series - Shape Reps: Parametric Meshes vs Implicit Functions (Gerard Pons-Moll) Deep Learning Day: Generative Modeling Neural Implicit Representations for 3D Vision - Prof. Andreas Geiger TUM AI Lecture Series - Neural Implicit Representations for 3D Vision (Andreas Geiger) Learning 3D Reconstruction in Function Space -- Andreas Geiger How Models Learn Without Training | In-Context Learning in Transformers changed AI Landscape Forever Learning Gradient Fields for Shape Generation Andreas Geiger: Learning 3D Reconstruction in Function Space Local Deep Implicit Functions for 3D Shapes Learning Implicit Templates for Point-Based Clothed Human Modeling, ECCV 2022 Lecture 13 | Generative Models Shape Modeling and Generative Modeling for Learning Dynamics - Hamid Krim Technion lecture 🤖Convolutional Neural Networks (CNNs) by #andrewtate and #donaldtrump Learning in Implicit Generative Models, NIPS 2016 | Shakir Mohamed, Google DeepMind Learning 3D Reconstruction in Function Space CSC2547 - Learning Gradient Fields for Shape Generation D.Schlesinger: Implicit Modeling — A Generalization of Discriminative and Generative Approaches.

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