GenXD: Generating Any 3D and 4D Scenes

• URL: http://arxiv.org/abs/2411.02319v2
• Date: Tue, 05 Nov 2024 06:08:43 GMT
• Title: GenXD: Generating Any 3D and 4D Scenes
• Authors: Yuyang Zhao, Chung-Ching Lin, Kevin Lin, Zhiwen Yan, Linjie Li, Zhengyuan Yang, Jianfeng Wang, Gim Hee Lee, Lijuan Wang,
• Abstract summary: We propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. By leveraging all the 3D and 4D data, we develop our framework, GenXD, which allows us to produce any 3D or 4D scene.
• Score: 137.5455092319533
• License:
• Abstract: Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. Due to the lack of real-world 4D data in the community, we first propose a data curation pipeline to obtain camera poses and object motion strength from videos. Based on this pipeline, we introduce a large-scale real-world 4D scene dataset: CamVid-30K. By leveraging all the 3D and 4D data, we develop our framework, GenXD, which allows us to produce any 3D or 4D scene. We propose multiview-temporal modules, which disentangle camera and object movements, to seamlessly learn from both 3D and 4D data. Additionally, GenXD employs masked latent conditions to support a variety of conditioning views. GenXD can generate videos that follow the camera trajectory as well as consistent 3D views that can be lifted into 3D representations. We perform extensive evaluations across various real-world and synthetic datasets, demonstrating GenXD's effectiveness and versatility compared to previous methods in 3D and 4D generation.

Related papers

• GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking [38.104532522698285]
  Training a video Diffusion Transformer (DiT) directly to control 4D content requires expensive multi-view videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS), we bring pseudo 4D Gaussian fields to video generation. We finetune a pretrained DiT to generate videos following the guidance of the rendered video, dubbed as GS-DiT.
  arXiv  Detail & Related papers  (2025-01-05T23:55:33Z)
• Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos [76.07894127235058]
  We present a system for mining high-quality 4D reconstructions from internet stereoscopic, wide-angle videos. We use this method to generate large-scale data in the form of world-consistent, pseudo-metric 3D point clouds. We demonstrate the utility of this data by training a variant of DUSt3R to predict structure and 3D motion from real-world image pairs.
  arXiv  Detail & Related papers  (2024-12-12T18:59:54Z)
• You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale [42.67300636733286]
  We present See3D, a visual-conditional multi-view diffusion model trained on large-scale Internet videos for open-world 3D creation. The model aims to Get 3D knowledge by solely Seeing the visual contents from the vast and rapidly growing video data. Our numerical and visual comparisons on single and sparse reconstruction benchmarks show that See3D, trained on cost-effective and scalable video data, achieves notable zero-shot and open-world generation capabilities.
  arXiv  Detail & Related papers  (2024-12-09T17:44:56Z)
• Segment Any 4D Gaussians [69.53172192552508]
  We propose Segment Any 4D Gaussians (SA4D) to segment anything in the 4D digital world based on 4D Gaussians. SA4D achieves precise, high-quality segmentation within seconds in 4D Gaussians and shows the ability to remove, recolor, compose, and render high-quality anything masks.
  arXiv  Detail & Related papers  (2024-07-05T13:44:15Z)
• Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text [61.9973218744157]
  We introduce Director3D, a robust open-world text-to-3D generation framework, designed to generate both real-world 3D scenes and adaptive camera trajectories. Experiments demonstrate that Director3D outperforms existing methods, offering superior performance in real-world 3D generation.
  arXiv  Detail & Related papers  (2024-06-25T14:42:51Z)
• OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation [107.71752592196138]
  We propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets. Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos.
  arXiv  Detail & Related papers  (2023-01-18T18:14:18Z)
• XDGAN: Multi-Modal 3D Shape Generation in 2D Space [60.46777591995821]
  We propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space. The generated geometry images are quick to convert to 3D meshes, enabling real-time 3D object synthesis, visualization and interactive editing. We show both quantitatively and qualitatively that our method is highly effective at various tasks such as 3D shape generation, single view reconstruction and shape manipulation, while being significantly faster and more flexible compared to recent 3D generative models.
  arXiv  Detail & Related papers  (2022-10-06T15:54:01Z)

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.