SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs

• URL: http://arxiv.org/abs/2406.06432v1
• Date: Mon, 10 Jun 2024 16:24:07 GMT
• Title: SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs
• Authors: Jing Yang, Kyle Fogarty, Fangcheng Zhong, Cengiz Oztireli,
• Abstract summary: We propose SYM3D, a novel 3D-aware GAN designed to leverage the prevalental symmetry structure found in natural and man-made objects. We evaluate SYM3D on both synthetic (ShapeNet Chairs, Cars, and Airplanes) and real-world datasets.
• Score: 5.84660008137615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the growing success of 3D-aware GANs, which can be trained on 2D images to generate high-quality 3D assets, they still rely on multi-view images with camera annotations to synthesize sufficient details from all viewing directions. However, the scarce availability of calibrated multi-view image datasets, especially in comparison to single-view images, has limited the potential of 3D GANs. Moreover, while bypassing camera pose annotations with a camera distribution constraint reduces dependence on exact camera parameters, it still struggles to generate a consistent orientation of 3D assets. To this end, we propose SYM3D, a novel 3D-aware GAN designed to leverage the prevalent reflectional symmetry structure found in natural and man-made objects, alongside a proposed view-aware spatial attention mechanism in learning the 3D representation. We evaluate SYM3D on both synthetic (ShapeNet Chairs, Cars, and Airplanes) and real-world datasets (ABO-Chair), demonstrating its superior performance in capturing detailed geometry and texture, even when trained on only single-view images. Finally, we demonstrate the effectiveness of incorporating symmetry regularization in helping reduce artifacts in the modeling of 3D assets in the text-to-3D task.

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)
• 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)
• GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning [67.61509647032862]
  We propose GOEmbed (Gradient Origin Embeddings) that encodes input 2D images into any 3D representation. Unlike typical prior approaches in which input images are encoded using 2D features extracted from large pre-trained models, or customized features are designed to handle different 3D representations.
  arXiv  Detail & Related papers  (2023-12-14T08:39:39Z)
• WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space [77.92350895927922]
  We propose WildFusion, a new approach to 3D-aware image synthesis based on latent diffusion models (LDMs) Our 3D-aware LDM is trained without any direct supervision from multiview images or 3D geometry. This opens up promising research avenues for scalable 3D-aware image synthesis and 3D content creation from in-the-wild image data.
  arXiv  Detail & Related papers  (2023-11-22T18:25:51Z)
• Uni3D: Exploring Unified 3D Representation at Scale [66.26710717073372]
  We present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild.
  arXiv  Detail & Related papers  (2023-10-10T16:49:21Z)
• Anything-3D: Towards Single-view Anything Reconstruction in the Wild [61.090129285205805]
  We introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segment-Anything object segmentation model. Our approach employs a BLIP model to generate textural descriptions, utilize the Segment-Anything model for the effective extraction of objects of interest, and leverages a text-to-image diffusion model to lift object into a neural radiance field.
  arXiv  Detail & Related papers  (2023-04-19T16:39:51Z)
• CC3D: Layout-Conditioned Generation of Compositional 3D Scenes [49.281006972028194]
  We introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts. Our evaluations on synthetic 3D-FRONT and real-world KITTI-360 datasets demonstrate that our model generates scenes of improved visual and geometric quality.
  arXiv  Detail & Related papers  (2023-03-21T17:59:02Z)
• Structured 3D Features for Reconstructing Controllable Avatars [43.36074729431982]
  We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface. We show that our S3F model surpasses the previous state-of-the-art on various tasks, including monocular 3D reconstruction, as well as albedo and shading estimation.
  arXiv  Detail & Related papers  (2022-12-13T18:57:33Z)
• 3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene [34.2144933185175]
  3inGAN is an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. We show results on semi-stochastic scenes of varying scale and complexity, obtained from real and synthetic sources.
  arXiv  Detail & Related papers  (2022-11-27T18:03:21Z)
• 3D GAN Inversion with Pose Optimization [26.140281977885376]
  We introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing.
  arXiv  Detail & Related papers  (2022-10-13T19:06:58Z)

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.