TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction

• URL: http://arxiv.org/abs/2412.16919v1
• Date: Sun, 22 Dec 2024 08:28:20 GMT
• Title: TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction
• Authors: Xuying Zhang, Yutong Liu, Yangguang Li, Renrui Zhang, Yufei Liu, Kai Wang, Wanli Ouyang, Zhiwei Xiong, Peng Gao, Qibin Hou, Ming-Ming Cheng,
• Abstract summary: TAR3D is a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) We show that TAR3D can achieve superior generation quality over existing methods in text-to-3D and image-to-3D tasks.
• Score: 137.34863114016483
• License:
• Abstract: We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets. The core insight of this work is to migrate the multimodal unification and promising learning capabilities of the next-token prediction paradigm to conditional 3D object generation. To achieve this, the 3D VQ-VAE first encodes a wide range of 3D shapes into a compact triplane latent space and utilizes a set of discrete representations from a trainable codebook to reconstruct fine-grained geometries under the supervision of query point occupancy. Then, the 3D GPT, equipped with a custom triplane position embedding called TriPE, predicts the codebook index sequence with prefilling prompt tokens in an autoregressive manner so that the composition of 3D geometries can be modeled part by part. Extensive experiments on ShapeNet and Objaverse demonstrate that TAR3D can achieve superior generation quality over existing methods in text-to-3D and image-to-3D tasks

Related papers

• Learning 3D Representations from Procedural 3D Programs [6.915871213703219]
  Self-supervised learning has emerged as a promising approach for acquiring transferable 3D representations from unlabeled 3D point clouds. We propose learning 3D representations from procedural 3D programs that automatically generate 3D shapes using simple primitives and augmentations.
  arXiv  Detail & Related papers  (2024-11-25T18:59:57Z)
• 3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation [45.218605449572586]
  3D-Adapter is a plug-in module designed to infuse 3D geometry awareness into pretrained image diffusion models. We show that 3D-Adapter greatly enhances the geometry quality of text-to-multi-view models such as Instant3D and Zero123++. We also showcase the broad application potential of 3D-Adapter by presenting high quality results in text-to-3D, image-to-3D, text-to-texture, and text-to-avatar tasks.
  arXiv  Detail & Related papers  (2024-10-24T17:59:30Z)
• Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation [27.43973967994717]
  MT3D is a text-to-3D generative model that leverages a high-fidelity 3D object to overcome viewpoint bias. By incorporating geometric details from a 3D asset, MT3D enables the creation of diverse and geometrically consistent objects.
  arXiv  Detail & Related papers  (2024-08-12T06:25:44Z)
• 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)
• IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation [96.32684334038278]
  In this paper, we explore the design space of text-to-3D models. We significantly improve multi-view generation by considering video instead of image generators. Our new method, IM-3D, reduces the number of evaluations of the 2D generator network 10-100x.
  arXiv  Detail & Related papers  (2024-02-13T18:59:51Z)
• 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)
• Large-Vocabulary 3D Diffusion Model with Transformer [57.076986347047]
  We introduce a diffusion-based feed-forward framework for synthesizing massive categories of real-world 3D objects with a single generative model. We propose a novel triplane-based 3D-aware Diffusion model with TransFormer, DiffTF, for handling challenges via three aspects. Experiments on ShapeNet and OmniObject3D convincingly demonstrate that a single DiffTF model achieves state-of-the-art large-vocabulary 3D object generation performance.
  arXiv  Detail & Related papers  (2023-09-14T17:59:53Z)
• 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)
• Learning 3D Scene Priors with 2D Supervision [37.79852635415233]
  We propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction.
  arXiv  Detail & Related papers  (2022-11-25T15:03:32Z)

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.