ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian Splatting

• URL: http://arxiv.org/abs/2407.19035v1
• Date: Fri, 26 Jul 2024 18:26:01 GMT
• Title: ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian Splatting
• Authors: Shen Chen, Jiale Zhou, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li,
• Abstract summary: The creation of high-quality 3D assets is paramount for applications in digital heritage, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for modeling. We introduce a novel 3D content creation framework, which generates 3D textures efficiently.
• Score: 30.99112626706754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The creation of high-quality 3D assets is paramount for applications in digital heritage preservation, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for the modeling, texturing, and rendering of 3D objects. However, the rising demand for 3D assets in gaming and virtual reality (VR) has led to the creation of accessible image-to-3D technologies, allowing non-professionals to produce 3D content and decreasing dependence on expert input. Existing methods for 3D content generation struggle to simultaneously achieve detailed textures and strong geometric consistency. We introduce a novel 3D content creation framework, ScalingGaussian, which combines 3D and 2D diffusion models to achieve detailed textures and geometric consistency in generated 3D assets. Initially, a 3D diffusion model generates point clouds, which are then densified through a process of selecting local regions, introducing Gaussian noise, followed by using local density-weighted selection. To refine the 3D gaussians, we utilize a 2D diffusion model with Score Distillation Sampling (SDS) loss, guiding the 3D Gaussians to clone and split. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error(MSE) and Gradient Profile Prior(GPP) losses. Our method addresses the common issue of sparse point clouds in 3D diffusion, resulting in improved geometric structure and detailed textures. Experiments on image-to-3D tasks demonstrate that our approach efficiently generates high-quality 3D assets.

Related papers

• Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors [17.544733016978928]
  3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Recent advancements in 3D object generation have introduced techniques that reconstruct an object's 3D shape and texture. We propose bridging the gap between 2D and 3D diffusion models to address this limitation.
  arXiv  Detail & Related papers  (2024-10-12T10:14:11Z)
• DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
  We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts. Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer [26.375689838055774]
  Direct3D is a native 3D generative model scalable to in-the-wild input images. Our approach comprises two primary components: a Direct 3D Variational Auto-Encoder (D3D-VAE) and a Direct 3D Diffusion Transformer (D3D-DiT)
  arXiv  Detail & Related papers  (2024-05-23T17:49:37Z)
• AGG: Amortized Generative 3D Gaussians for Single Image to 3D [108.38567665695027]
  We introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image. AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization. We propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module.
  arXiv  Detail & Related papers  (2024-01-08T18:56:33Z)
• GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models [102.22388340738536]
  2D and 3D diffusion models can generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation.
  arXiv  Detail & Related papers  (2023-10-12T17:22:24Z)
• Text-to-3D using Gaussian Splatting [18.163413810199234]
  This paper proposes GSGEN, a novel method that adopts Gaussian Splatting, a recent state-of-the-art representation, to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting. Our approach can generate 3D assets with delicate details and accurate geometry.
  arXiv  Detail & Related papers  (2023-09-28T16:44:31Z)
• ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections [71.46546520120162]
  Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging. We propose ARTIC3D, a self-supervised framework to reconstruct per-instance 3D shapes from a sparse image collection in-the-wild. We produce realistic animations by fine-tuning the rendered shape and texture under rigid part transformations.
  arXiv  Detail & Related papers  (2023-06-07T17:47:50Z)
• 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)
• Deep Hybrid Self-Prior for Full 3D Mesh Generation [57.78562932397173]
  We propose to exploit a novel hybrid 2D-3D self-prior in deep neural networks to significantly improve the geometry quality. In particular, we first generate an initial mesh using a 3D convolutional neural network with 3D self-prior, and then encode both 3D information and color information in the 2D UV atlas. Our method recovers the 3D textured mesh model of high quality from sparse input, and outperforms the state-of-the-art methods in terms of both the geometry and texture quality.
  arXiv  Detail & Related papers  (2021-08-18T07:44:21Z)

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.