Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention

• URL: http://arxiv.org/abs/2311.17834v4
• Date: Wed, 8 May 2024 08:37:22 GMT
• Title: Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention
• Authors: Etai Sella, Gal Fiebelman, Noam Atia, Hadar Averbuch-Elor,
• Abstract summary: Spice-E is a neural network that adds structural guidance to 3D diffusion models. We show that our approach supports a variety of applications, including 3D stylization, semantic shape editing and text-conditional abstraction-to-3D.
• Score: 9.52027244702166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are witnessing rapid progress in automatically generating and manipulating 3D assets due to the availability of pretrained text-image diffusion models. However, time-consuming optimization procedures are required for synthesizing each sample, hindering their potential for democratizing 3D content creation. Conversely, 3D diffusion models now train on million-scale 3D datasets, yielding high-quality text-conditional 3D samples within seconds. In this work, we present Spice-E - a neural network that adds structural guidance to 3D diffusion models, extending their usage beyond text-conditional generation. At its core, our framework introduces a cross-entity attention mechanism that allows for multiple entities (in particular, paired input and guidance 3D shapes) to interact via their internal representations within the denoising network. We utilize this mechanism for learning task-specific structural priors in 3D diffusion models from auxiliary guidance shapes. We show that our approach supports a variety of applications, including 3D stylization, semantic shape editing and text-conditional abstraction-to-3D, which transforms primitive-based abstractions into highly-expressive shapes. Extensive experiments demonstrate that Spice-E achieves SOTA performance over these tasks while often being considerably faster than alternative methods. Importantly, this is accomplished without tailoring our approach for any specific task.

Related papers

• Any-to-3D Generation via Hybrid Diffusion Supervision [67.54197818071464]
  XBind is a unified framework for any-to-3D generation using cross-modal pre-alignment techniques. XBind integrates an multimodal-aligned encoder with pre-trained diffusion models to generate 3D objects from any modalities.
  arXiv  Detail & Related papers  (2024-11-22T03:52:37Z)
• LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation [73.36690511083894]
  This paper introduces a novel framework called LN3Diff to address a unified 3D diffusion pipeline. Our approach harnesses a 3D-aware architecture and variational autoencoder to encode the input image into a structured, compact, and 3D latent space. It achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation.
  arXiv  Detail & Related papers  (2024-03-18T17:54:34Z)
• 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)
• VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder [56.59814904526965]
  This paper introduces a pioneering 3D encoder designed for text-to-3D generation. A lightweight network is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net.
  arXiv  Detail & Related papers  (2023-12-18T18:59:05Z)
• TPA3D: Triplane Attention for Fast Text-to-3D Generation [28.33270078863519]
  We propose Triplane Attention for text-guided 3D generation (TPA3D) TPA3D is an end-to-end trainable GAN-based deep learning model for fast text-to-3D generation. We show that TPA3D generates high-quality 3D textured shapes aligned with fine-grained descriptions.
  arXiv  Detail & Related papers  (2023-12-05T10:39:37Z)
• DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields [68.94868475824575]
  This paper introduces a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations. We leverage the strong semantic prior within a 3D generative model to train a semantic decoder. Once trained, the decoder efficiently generalizes across the latent space, enabling the generation of infinite data.
  arXiv  Detail & Related papers  (2023-11-18T21:58:28Z)
• 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
  3DiffTection is a state-of-the-art method for 3D object detection from single images. We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image. We further train the model on target data with detection supervision.
  arXiv  Detail & Related papers  (2023-11-07T23:46:41Z)
• Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution [28.526714129927093]
  We propose DreamPortrait, which aims to generate text-guided 3D-aware portraits in a single-forward pass for efficiency. We further design a 3D-aware gated cross-attention mechanism to explicitly let the model perceive the correspondence between the text and the 3D-aware space.
  arXiv  Detail & Related papers  (2023-06-03T11:08:38Z)
• 3D Neural Field Generation using Triplane Diffusion [37.46688195622667]
  We present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.
  arXiv  Detail & Related papers  (2022-11-30T01:55:52Z)

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.