Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation

• URL: http://arxiv.org/abs/2408.05938v1
• Date: Mon, 12 Aug 2024 06:25:44 GMT
• Title: Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation
• Authors: Utkarsh Nath, Rajeev Goel, Eun Som Jeon, Changhoon Kim, Kyle Min, Yezhou Yang, Yingzhen Yang, Pavan Turaga,
• Abstract summary: MT3D is a text-to-3D generative model that leverages a high-fidelity 3D object to overcome viewpoint bias. We employ depth maps derived from a high-quality 3D model as control signals to guarantee that the generated 2D images preserve the fundamental shape and structure. By incorporating geometric details from a 3D asset, MT3D enables the creation of diverse and geometrically consistent objects.
• Score: 27.43973967994717
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: To address the data scarcity associated with 3D assets, 2D-lifting techniques such as Score Distillation Sampling (SDS) have become a widely adopted practice in text-to-3D generation pipelines. However, the diffusion models used in these techniques are prone to viewpoint bias and thus lead to geometric inconsistencies such as the Janus problem. To counter this, we introduce MT3D, a text-to-3D generative model that leverages a high-fidelity 3D object to overcome viewpoint bias and explicitly infuse geometric understanding into the generation pipeline. Firstly, we employ depth maps derived from a high-quality 3D model as control signals to guarantee that the generated 2D images preserve the fundamental shape and structure, thereby reducing the inherent viewpoint bias. Next, we utilize deep geometric moments to ensure geometric consistency in the 3D representation explicitly. By incorporating geometric details from a 3D asset, MT3D enables the creation of diverse and geometrically consistent objects, thereby improving the quality and usability of our 3D representations.

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)
• GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation [65.33726478659304]
  We introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D structure and do not utilize explicit geometric relationships between 3D and 2D images. GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms.
  arXiv  Detail & Related papers  (2024-06-21T17:49:31Z)
• 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)
• GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting [81.03553265684184]
  We introduce GeoGS3D, a framework for reconstructing detailed 3D objects from single-view images. We propose a novel metric, Gaussian Divergence Significance (GDS), to prune unnecessary operations during optimization. Experiments demonstrate that GeoGS3D generates images with high consistency across views and reconstructs high-quality 3D objects.
  arXiv  Detail & Related papers  (2024-03-15T12:24:36Z)
• Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior [57.986512832738704]
  We present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model.
  arXiv  Detail & Related papers  (2024-03-14T07:39:59Z)
• Retrieval-Augmented Score Distillation for Text-to-3D Generation [30.57225047257049]
  We introduce novel framework for retrieval-based quality enhancement in text-to-3D generation. We conduct extensive experiments to demonstrate that ReDream exhibits superior quality with increased geometric consistency.
  arXiv  Detail & Related papers  (2024-02-05T12:50:30Z)
• Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior [52.44678180286886]
  2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. We propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously.
  arXiv  Detail & Related papers  (2023-12-11T18:59:18Z)
• GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation [66.46683554587352]
  We present GeoDream, a novel method that incorporates explicit generalized 3D priors with 2D diffusion priors. Specifically, we first utilize a multi-view diffusion model to generate posed images and then construct cost volume from the predicted image. We further propose to harness 3D geometric priors to unlock the great potential of 3D awareness in 2D diffusion priors via a disentangled design.
  arXiv  Detail & Related papers  (2023-11-29T15:48:48Z)
• Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation [16.232803881159022]
  We propose a flexible framework of Points-to-3D to bridge the gap between sparse yet freely available 3D points and realistic shape-controllable 3D generation. The core idea of Points-to-3D is to introduce controllable sparse 3D points to guide the text-to-3D generation.
  arXiv  Detail & Related papers  (2023-07-26T02:16:55Z)
• Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [70.71934539556916]
  We learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection. Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised. Our method remarkably improves the detection performance of the state-of-the-art monocular-based method without extra data by 2.80% on the moderate test setting.
  arXiv  Detail & Related papers  (2021-07-29T12:30:39Z)

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.