Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

• URL: http://arxiv.org/abs/2606.07117v1
• Date: Fri, 05 Jun 2026 10:13:17 GMT
• Title: Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment
• Abstract summary: Native3D is the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations.<n>We propose a unified mesh-texture joint representation that simultaneously models both geometric structures and texture features through a Transformer-based scene encoder.
• Score: 8.98466538269363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations. Traditional approaches typically require adapting 3D representations to the 2D domain to leverage pre-trained diffusion models, which inevitably introduces domain adaptation issues including geometric structural distortion and texture detail degradation. To address these limitations, we design a unified mesh-texture joint representation that simultaneously models both geometric structures and texture features through a Transformer-based scene encoder, effectively maintaining spatial relationships and visual consistency among objects within scenes. We further propose the 3D Representation Alignment Loss (3D REPA Loss), which employs an improved contrastive learning mechanism to align multi-level semantic representations in the latent space, significantly enhancing geometric and textural fidelity. Experimental results demonstrate that Native3D outperforms existing methods in both generation quality and editing flexibility, providing a novel solution for 3D scene editing.

Related papers

• TAPESTRY: From Geometry to Appearance via Consistent Turntable Videos [65.99602532894241]
  We introduce TAPESTRY, a framework for generating high-fidelity TTVs conditioned on explicit 3D geometry.<n>We also design a method for downstream reconstruction tasks from the TTV input, featuring a multi-stage pipeline with 3D-Aware Inpainting.<n>Results demonstrate that our method outperforms existing approaches in both video consistency and final reconstruction quality.
  arXiv  Detail & Related papers  (2026-03-18T14:02:09Z)
• OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder [90.8453349494245]
  We present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space.<n>OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods.
  arXiv  Detail & Related papers  (2026-03-17T03:43:37Z)
• GeoDiff3D: Self-Supervised 3D Scene Generation with Geometry-Constrained 2D Diffusion Guidance [8.625308061265754]
  3D scene generation is a core technology for gaming, film/VFX, and VR/AR.<n>Existing methods broadly follow two paradigms - indirect 2D-to-3D reconstruction and direct 3D generation.<n>We propose GeoDiff3D, an efficient self-supervised framework that uses coarse geometry as a structural anchor and a geometry-constrained 2D diffusion model to provide texture-rich reference images.
  arXiv  Detail & Related papers  (2026-01-27T16:47:35Z)
• Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing [63.141976759536625]
  We propose Interp3D, a training-free framework for textured 3D morphing.<n>It harnesses generative priors and adopts a progressive alignment principle to ensure both geometric fidelity and texture coherence.<n>For comprehensive evaluations, we construct a dedicated dataset, Interp3DData, with graded difficulty levels and assess generation results from fidelity, transition smoothness, and plausibility.
  arXiv  Detail & Related papers  (2026-01-20T16:03:22Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification [59.17489431187807]
  We propose a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics.<n>Our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias.
  arXiv  Detail & Related papers  (2025-09-18T13:45:08Z)
• Advancing high-fidelity 3D and Texture Generation with 2.5D latents [21.33523572280285]
  We propose a novel framework for joint generation of 3D geometry and texture.<n>Specifically, we focus in generate a versatile 2.5D representations that can be seamlessly transformed between 2D and 3D.<n>Our model not only excels in generating high-quality 3D objects with coherent structure and color from text and image inputs but also significantly outperforms existing methods in geometry-conditioned texture generation.
  arXiv  Detail & Related papers  (2025-05-27T11:35:35Z)
• Constructing a 3D Scene from a Single Image [31.11317559252235]
  SceneFuse-3D is a training-free framework designed to synthesize coherent 3D scenes from a single top-down view.<n>We decompose the input image into overlapping regions and generate each using a pretrained 3D object generator.<n>This modular design allows us to overcome resolution bottlenecks and preserve spatial structure without requiring 3D supervision or fine-tuning.
  arXiv  Detail & Related papers  (2025-05-21T17:10:47Z)
• Shape from Semantics: 3D Shape Generation from Multi-View Semantics [30.969299308083723]
  Existing 3D reconstruction methods utilize guidances such as 2D images, 3D point clouds, shape contours and single semantics to recover the 3D surface.<n>We propose a novel 3D modeling task called Shape from Semantics'', which aims to create 3D models whose geometry and appearance are consistent with the given text semantics when viewed from different views.
  arXiv  Detail & Related papers  (2025-02-01T07:51:59Z)
• VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing [22.39760469467524]
  We propose a Variance texture synthesis to address the modal gap between the 2D and 3D diffusion models. We present an inpainting module to improve details with conflicting regions.
  arXiv  Detail & Related papers  (2024-07-05T12:11:33Z)
• Towards Realistic 3D Embedding via View Alignment [53.89445873577063]
  This paper presents an innovative View Alignment GAN (VA-GAN) that composes new images by embedding 3D models into 2D background images realistically and automatically. VA-GAN consists of a texture generator and a differential discriminator that are inter-connected and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-07-14T14:45:00Z)

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.