Shape from Semantics: 3D Shape Generation from Multi-View Semantics

• URL: http://arxiv.org/abs/2502.00360v1
• Date: Sat, 01 Feb 2025 07:51:59 GMT
• Title: Shape from Semantics: 3D Shape Generation from Multi-View Semantics
• Authors: Liangchen Li, Caoliwen Wang, Yuqi Zhou, Bailin Deng, Juyong Zhang,
• Abstract summary: Shape from Semantics'' is able to create 3D models whose geometry and appearance match given semantics when observed from different views. Our framework generates meshes with complex details, well-structured geometry, coherent textures, and smooth transitions.
• Score: 30.969299308083723
• License:
• Abstract: We propose ``Shape from Semantics'', which is able to create 3D models whose geometry and appearance match given semantics when observed from different views. Traditional ``Shape from X'' tasks usually use visual input (e.g., RGB images or depth maps) to reconstruct geometry, imposing strict constraints that limit creative explorations. As applications, works like Shadow Art and Wire Art often struggle to grasp the embedded semantics of their design through direct observation and rely heavily on specific setups for proper display. To address these limitations, our framework uses semantics as input, greatly expanding the design space to create objects that integrate multiple semantic elements and are easily discernible by observers. Considering that this task requires a rich imagination, we adopt various generative models and structure-to-detail pipelines. Specifically, we adopt multi-semantics Score Distillation Sampling (SDS) to distill 3D geometry and appearance from 2D diffusion models, ensuring that the initial shape is consistent with the semantic input. We then use image restoration and video generation models to add more details as supervision. Finally, we introduce neural signed distance field (SDF) representation to achieve detailed shape reconstruction. Our framework generates meshes with complex details, well-structured geometry, coherent textures, and smooth transitions, resulting in visually appealing and eye-catching designs. Project page: https://shapefromsemantics.github.io

Related papers

• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors [11.118490283303407]
  We propose a neural field semantic reconstruction approach to lift inferred image-level noisy priors to 3D. Our method produces accurate semantics and geometry in both 3D and 2D space.
  arXiv  Detail & Related papers  (2024-09-21T05:12:13Z)
• GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions [22.077366472693395]
  We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. By employing volumetric rendering using neural radiance fields, they inherit a key limitation: the generated geometry is noisy and unconstrained. We propose GeoGen, a new SDF-based 3D generative model trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-06-06T17:00:10Z)
• ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning [125.90002884194838]
  ConceptGraphs is an open-vocabulary graph-structured representation for 3D scenes. It is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. We demonstrate the utility of this representation through a number of downstream planning tasks.
  arXiv  Detail & Related papers  (2023-09-28T17:53:38Z)
• MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation [91.6658845016214]
  We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework. As a result, the learned 2D representations are view-invariant and geometrically consistent.
  arXiv  Detail & Related papers  (2022-08-18T00:48:15Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)
• 3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow [61.62796058294777]
  Reconstructing 3D shape from a single 2D image is a challenging task. Most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. We propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images.
  arXiv  Detail & Related papers  (2022-03-29T02:03:31Z)
• Facial Geometric Detail Recovery via Implicit Representation [147.07961322377685]
  We present a robust texture-guided geometric detail recovery approach using only a single in-the-wild facial image. Our method combines high-quality texture completion with the powerful expressiveness of implicit surfaces. Our method not only recovers accurate facial details but also decomposes normals, albedos, and shading parts in a self-supervised way.
  arXiv  Detail & Related papers  (2022-03-18T01:42:59Z)
• Topologically Consistent Multi-View Face Inference Using Volumetric Sampling [25.001398662643986]
  ToFu is a geometry inference framework that can produce topologically consistent meshes across identities and expressions. A novel progressive mesh generation network embeds the topological structure of the face in a feature volume. These high-quality assets are readily usable by production studios for avatar creation, animation and physically-based skin rendering.
  arXiv  Detail & Related papers  (2021-10-06T17:55:08Z)

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.