Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed

• URL: http://arxiv.org/abs/2311.17109v1
• Date: Tue, 28 Nov 2023 10:51:05 GMT
• Title: Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed
• Authors: Urs Waldmann, Ole Johannsen, Bastian Goldluecke
• Abstract summary: We present a neural rendering pipeline for textured articulated shapes that we call Neural Texture Puppeteer. A texture auto-encoder makes use of this information to encode textured images into a global latent code. Our method can be applied to endangered species where data is limited.
• Score: 2.8544822698499255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a neural rendering pipeline for textured articulated shapes that we call Neural Texture Puppeteer. Our method separates geometry and texture encoding. The geometry pipeline learns to capture spatial relationships on the surface of the articulated shape from ground truth data that provides this geometric information. A texture auto-encoder makes use of this information to encode textured images into a global latent code. This global texture embedding can be efficiently trained separately from the geometry, and used in a downstream task to identify individuals. The neural texture rendering and the identification of individuals run at interactive speeds. To the best of our knowledge, we are the first to offer a promising alternative to CNN- or transformer-based approaches for re-identification of articulated individuals based on neural rendering. Realistic looking novel view and pose synthesis for different synthetic cow textures further demonstrate the quality of our method. Restricted by the availability of ground truth data for the articulated shape's geometry, the quality for real-world data synthesis is reduced. We further demonstrate the flexibility of our model for real-world data by applying a synthetic to real-world texture domain shift where we reconstruct the texture from a real-world 2D RGB image. Thus, our method can be applied to endangered species where data is limited. Our novel synthetic texture dataset NePuMoo is publicly available to inspire further development in the field of neural rendering-based re-identification.

Related papers

• Real-time Free-view Human Rendering from Sparse-view RGB Videos using Double Unprojected Textures [87.80984588545589]
  Real-time free-view human rendering from sparse-view RGB inputs is a challenging task due to the sensor scarcity and the tight time budget. Recent methods leverage 2D CNNs operating in texture space to learn rendering primitives. We present Double Unprojected Textures, which at the core disentangles coarse geometric deformation estimation from appearance synthesis.
  arXiv  Detail & Related papers  (2024-12-17T18:57:38Z)
• NeRF-Texture: Synthesizing Neural Radiance Field Textures [77.24205024987414]
  We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. We can synthesize NeRF-based textures through patch matching of latent features.
  arXiv  Detail & Related papers  (2024-12-13T09:41:48Z)
• TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion [64.49276500129092]
  TextureDreamer is an image-guided texture synthesis method. It can transfer relightable textures from a small number of input images to target 3D shapes across arbitrary categories.
  arXiv  Detail & Related papers  (2024-01-17T18:55:49Z)
• DNS SLAM: Dense Neural Semantic-Informed SLAM [92.39687553022605]
  DNS SLAM is a novel neural RGB-D semantic SLAM approach featuring a hybrid representation. Our method integrates multi-view geometry constraints with image-based feature extraction to improve appearance details. Our experimental results achieve state-of-the-art performance on both synthetic data and real-world data tracking.
  arXiv  Detail & Related papers  (2023-11-30T21:34:44Z)
• NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing [39.71252429542249]
  We present a novel mesh-based representation by encoding the neural implicit field with disentangled geometry and texture codes on mesh vertices. We develop several techniques including learnable sign indicators to magnify spatial distinguishability of mesh-based representation. Experiments and editing examples on both real and synthetic data demonstrate the superiority of our method on representation quality and editing ability.
  arXiv  Detail & Related papers  (2022-07-25T05:30:50Z)
• GeoNeRF: Generalizing NeRF with Geometry Priors [2.578242050187029]
  We present GeoNeRF, a generalizable photorealistic novel view method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a synthesis. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets.
  arXiv  Detail & Related papers  (2021-11-26T15:15:37Z)
• Neural Re-Rendering of Humans from a Single Image [80.53438609047896]
  We propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image.
  arXiv  Detail & Related papers  (2021-01-11T18:53:47Z)
• Deep Geometric Texture Synthesis [83.9404865744028]
  We propose a novel framework for synthesizing geometric textures. It learns texture statistics from local neighborhoods of a single reference 3D model. Our network displaces mesh vertices in any direction, enabling synthesis of geometric textures.
  arXiv  Detail & Related papers  (2020-06-30T19:36:38Z)
• Co-occurrence Based Texture Synthesis [25.4878061402506]
  We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images. We show that our solution offers a stable, intuitive and interpretable latent representation for texture synthesis.
  arXiv  Detail & Related papers  (2020-05-17T08:01:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.