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CAT3D: Create Anything in 3D with Multi-View Diffusion Models (2405.10314v1)

Published 16 May 2024 in cs.CV

Abstract: Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world capture process with a multi-view diffusion model. Given any number of input images and a set of target novel viewpoints, our model generates highly consistent novel views of a scene. These generated views can be used as input to robust 3D reconstruction techniques to produce 3D representations that can be rendered from any viewpoint in real-time. CAT3D can create entire 3D scenes in as little as one minute, and outperforms existing methods for single image and few-view 3D scene creation. See our project page for results and interactive demos at https://cat3d.github.io .

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Authors (8)
  1. Ruiqi Gao (44 papers)
  2. Aleksander Holynski (37 papers)
  3. Philipp Henzler (18 papers)
  4. Arthur Brussee (5 papers)
  5. Ricardo Martin-Brualla (28 papers)
  6. Pratul Srinivasan (8 papers)
  7. Jonathan T. Barron (89 papers)
  8. Ben Poole (46 papers)
Citations (68)
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Summary

CAT3D: Creating 3D Scenes from Images with Multi-View Diffusion Models

Introduction

Imagine creating a highly detailed 3D scene from just one or a few images. Sounds like magic? Well, CAT3D essentially promises this by leveraging a multi-view diffusion model to generate a collection of consistent novel views of a 3D scene. This paper discusses how CAT3D achieves that by simulating the real-world capture process and generating high-quality 3D content significantly faster than existing methods.

How CAT3D Works

CAT3D is a two-step approach:

  1. Novel View Generation: The model takes any number of input views and generates multiple 3D-consistent images from specified novel viewpoints.
  2. 3D Reconstruction: These generated views are then used as input to robust 3D reconstruction techniques to produce a 3D representation that can be rendered interactively from any viewpoint.

Step 1: Novel View Generation

The core component here is the multi-view diffusion model, which is trained to generate novel views that are consistent with a given set of input views. The model utilizes:

  • 3D Self-Attention: By capturing dependencies across multiple views, it produces consistent and high-fidelity images.
  • Camera Raymaps: These encode camera position information directly into each image, offering a robust way to handle various camera angles.

The model clusters target viewpoints into smaller groups, initially generating a set of anchor views before expanding outwards in parallel. This strategy ensures efficiency and maintains consistency across generated views.

Step 2: 3D Reconstruction

Once the novel views are generated, CAT3D employs a reconstruction pipeline, drawing from techniques like NeRF (Neural Radiance Fields), which enables the rendering of highly detailed 3D structures. This pipeline has been enhanced to handle potential inconsistencies in the generated images, making it even more robust.

Key Results

The paper presents some impressive quantitative metrics and qualitative results:

  • Few-View Reconstruction: Across multiple benchmark datasets, CAT3D outperforms existing methods like ReconFusion and ZeroNVS in terms of metrics like PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and LPIPS (Learned Perceptual Image Patch Similarity).
  • Speed: While comparable methods might take up to an hour for processing, CAT3D accomplishes this task in mere minutes, marking significant efficiency improvements.

Practical and Theoretical Implications

Practical Implications

  1. Gaming and Animation: The ability to quickly generate high-quality 3D content makes CAT3D particularly useful for real-time applications like gaming and animation.
  2. Virtual and Augmented Reality: CAT3D could simplify the creation of environments for VR and AR, where rapid and dynamic 3D scene generation is key.

Theoretical Implications

  1. Multi-View Diffusion Models: This work demonstrates the potential of multi-view diffusion models in synthesizing consistent novel views, pushing the boundaries of 3D scene reconstruction.
  2. Robust 3D Reconstruction: By refining 3D reconstruction techniques to handle inconsistencies in generated views, the paper contributes to making these methods more generally applicable and robust.

Future of AI in 3D Reconstruction

The results indicate that we are moving towards more accessible and efficient 3D content creation from minimal input. Future developments could include:

  • Enhanced Consistency: Future models might further reduce inconsistencies between generated views, making the reconstruction process even more robust.
  • Real-Time Applications: With continued efficiency improvements, we might see real-time implementations in consumer devices, significantly impacting areas like telepresence and remote collaboration.

In conclusion, CAT3D represents a significant step forward in 3D scene generation by leveraging innovative diffusion models and robust reconstruction techniques. Whether for creating immersive VR experiences or simplifying game development, this approach promises to make high-quality 3D content more accessible than ever before.

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