Efficient 3D Content Reconstruction and Generation

• URL: http://arxiv.org/abs/2605.18052v1
• Date: Mon, 18 May 2026 08:41:10 GMT
• Title: Efficient 3D Content Reconstruction and Generation
• Abstract summary: I introduce Instant3D, which combines multi-view diffusion with feed-forward sparse-view 3D reconstruction to produce high-quality assets in 5-20 seconds.<n>I develop FastMap, a structure-from-motion pipeline that achieves up to 10x speedup over prior state-of-the-art.
• Score: 5.8565664251165375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic 3D content creation seeks to replace labor-intensive modeling and scanning pipelines with systems that can synthesize or recover 3D assets directly from text or images. Its applications span video games, virtual reality, robotics, and simulation, enabling rapid asset prototyping, diverse interactive world generation, and efficient 3D data collection for training foundation models. Contemporary solutions largely follow two complementary paradigms: (i) text- or image-to-3D generation, which learns priors over 3D geometry and appearance to create novel assets from natural language or a single view image; and (ii) 3D reconstruction, which estimates camera poses and geometry from RGB images. This thesis advances both directions. On the generation side, I introduce Instant3D, which combines multi-view diffusion with feed-forward sparse-view 3D reconstruction to produce high-quality assets in 5-20 seconds. On the reconstruction side, I develop FastMap, a structure-from-motion pipeline that achieves up to 10x speedup over prior state-of-the-art by using first-order optimization with fused GPU kernels extensively, while maintaining comparable pose accuracy and downstream novel view synthesis quality.

Related papers

• 3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis [35.43901502343984]
  3DCarGen is a scalable single-view 3D car generation framework for real-world images.<n>Our approach achieves robust geometric consistency and reconstruction fidelity compared to existing methods.
  arXiv  Detail & Related papers  (2026-06-23T07:44:14Z)
• Self-Evolving 3D Scene Generation from a Single Image [44.87957263540352]
  EvoScene is a training-free framework that progressively reconstructs complete 3D scenes from single images.<n>EvoScene alternates between 2D and 3D domains, gradually improving both structure and appearance.
  arXiv  Detail & Related papers  (2025-12-09T18:44:21Z)
• 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)
• Magic-Boost: Boost 3D Generation with Multi-View Conditioned Diffusion [101.15628083270224]
  We propose a novel multi-view conditioned diffusion model to synthesize high-fidelity novel view images.<n>We then introduce a novel iterative-update strategy to adopt it to provide precise guidance to refine the coarse generated results.<n>Experiments show Magic-Boost greatly enhances the coarse generated inputs, generates high-quality 3D assets with rich geometric and textural details.
  arXiv  Detail & Related papers  (2024-04-09T16:20:03Z)
• IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation [96.32684334038278]
  In this paper, we explore the design space of text-to-3D models. We significantly improve multi-view generation by considering video instead of image generators. Our new method, IM-3D, reduces the number of evaluations of the 2D generator network 10-100x.
  arXiv  Detail & Related papers  (2024-02-13T18:59:51Z)
• Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model [68.98311213582949]
  We propose Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner. Our method can generate diverse 3D assets of high visual quality within 20 seconds, two orders of magnitude faster than previous optimization-based methods.
  arXiv  Detail & Related papers  (2023-11-10T18:03:44Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images [72.15855070133425]
  We introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings.
  arXiv  Detail & Related papers  (2022-09-22T17:16:19Z)
• 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.