DreamPolish: Domain Score Distillation With Progressive Geometry Generation

• URL: http://arxiv.org/abs/2411.01602v1
• Date: Sun, 03 Nov 2024 15:15:01 GMT
• Title: DreamPolish: Domain Score Distillation With Progressive Geometry Generation
• Authors: Yean Cheng, Ziqi Cai, Ming Ding, Wendi Zheng, Shiyu Huang, Yuxiao Dong, Jie Tang, Boxin Shi,
• Abstract summary: We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain.
• Score: 66.94803919328815
• License:
• Abstract: We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. Instead of relying solely on a view-conditioned diffusion prior in the novel sampled views, which often leads to undesired artifacts in the geometric surface, we incorporate an additional normal estimator to polish the geometry details, conditioned on viewpoints with varying field-of-views. We propose to add a surface polishing stage with only a few training steps, which can effectively refine the artifacts attributed to limited guidance from previous stages and produce 3D objects with more desirable geometry. The key topic of texture generation using pretrained text-to-image models is to find a suitable domain in the vast latent distribution of these models that contains photorealistic and consistent renderings. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain. We draw inspiration from the classifier-free guidance (CFG) in textconditioned image generation tasks and show that CFG and variational distribution guidance represent distinct aspects in gradient guidance and are both imperative domains for the enhancement of texture quality. Extensive experiments show our proposed model can produce 3D assets with polished surfaces and photorealistic textures, outperforming existing state-of-the-art methods.

Related papers

• 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)
• G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images [45.66479596827045]
  We propose a Geometry-enhanced NeRF (G-NeRF), which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach. To tackle the absence of multi-view supervision for single-view images, we design the depth-aware training approach.
  arXiv  Detail & Related papers  (2024-04-11T04:58:18Z)
• Breathing New Life into 3D Assets with Generative Repainting [74.80184575267106]
  Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators. Recent works have proposed various pipelines powered by the entanglement of diffusion models and neural fields. We explore the power of pretrained 2D diffusion models and standard 3D neural radiance fields as independent, standalone tools. Our pipeline accepts any legacy renderable geometry, such as textured or untextured meshes, and orchestrates the interaction between 2D generative refinement and 3D consistency enforcement tools.
  arXiv  Detail & Related papers  (2023-09-15T16:34:51Z)
• 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)
• Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement [78.48648360358193]
  We present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency appearance with a NeRF. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering.
  arXiv  Detail & Related papers  (2023-03-03T17:14:44Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)
• Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction [0.0]
  Learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications. We present a novel sampling algorithm by optimizing the gradient of predicted coordinates based on the variance on the sampling image. We also adopt Frechet Inception Distance (FID) to form a loss function in learning, which helps bridging the gap between rendered images and input images.
  arXiv  Detail & Related papers  (2021-04-29T07:52:54Z)
• Learning Generative Models of Textured 3D Meshes from Real-World Images [26.353307246909417]
  We propose a GAN framework for generating textured triangle meshes without relying on such annotations. We show that the performance of our approach is on par with prior work that relies on ground-truth keypoints.
  arXiv  Detail & Related papers  (2021-03-29T14:07:37Z)
• Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images [64.53227129573293]
  We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. We design neural networks capable of generating high-quality parametric 3D surfaces which are consistent between views. Our method is supervised and trained on a public dataset of shapes from common object categories.
  arXiv  Detail & Related papers  (2020-08-18T06:33:40Z)

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.