Generating Realistic Images with 3D Annotations Using Diffusion Models at ICLR 2024

Core Concepts

Diffusion models integrated with 3D geometry control enhance image generation and annotation acquisition.

Abstract

The paper introduces 3D Diffusion Style Transfer (3D-DST) to incorporate 3D geometry control into diffusion models for generating realistic images with detailed 3D annotations. The method, ControlNet, extends diffusion models by using visual prompts in addition to text prompts. By rendering images of 3D objects from various viewpoints and distances, edge maps are computed as visual prompts. These edge maps enable explicit control over the 3D structure of the objects in the generated images. Text prompts are enhanced using large language models (LLM) to improve diversity further. The study demonstrates significant improvements in image classification accuracy on ImageNet-100 and ImageNet-R datasets using pre-trained models on 3D-DST synthetic data. Additionally, the method enhances performance in tasks like 3D pose estimation and object detection across both in-distribution (ID) and out-of-distribution (OOD) settings.

Stats

Our method significantly outperforms existing methods by 3.8 percentage points on ImageNet-100 using DeiT-B. Objaverse contains a repository of 800K CAD models while Objaverse-XL expands to more than 10 million 3D objects. Pre-training on generated images from our method improves the accuracy of DeiT-S on ImageNet-200 by 3.31 percentage points.

Quotes

"Our method exploits ControlNet, which extends diffusion models by using visual prompts in addition to text prompts." "With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images." "Our code is available at https://ccvl.jhu.edu/3D-DST/"

Key Insights Distilled From

Generating Images with 3D Annotations Using Diffusion Models

by Wufei Ma,Qih... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2306.08103.pdf

Deeper Inquiries

How can incorporating LLM prompts enhance diversity in image generation?

Incorporating Large Language Models (LLM) prompts can enhance diversity in image generation by providing rich and varied textual descriptions that guide the generation process. LLMs are capable of generating detailed and coherent descriptions, such as backgrounds, colors, and other attributes, based on the initial text prompts provided to them. By combining class names with associated tags or keywords from CAD models and then enhancing these prompts with LLM-generated descriptions, the resulting text prompts become more dynamic and diverse. These diverse text prompts help diffusion models generate images with a wide range of appearances, backgrounds, weather conditions, colors, and other visual characteristics. This approach not only adds variety to the generated images but also helps avoid producing redundant or similar outputs. The combination of 3D geometry control through visual prompts along with diverse text prompts from LLMs results in a more versatile and varied set of generated images.

How does our approach impact the robustness of AI models across different datasets?

Our approach impacts the robustness of AI models across different datasets by introducing explicit control over 3D structures into diffusion models through 3D-DST (3D Diffusion Style Transfer). By incorporating ControlNet for visual prompting alongside large language model (LLM) text prompts, we enable precise manipulation of 3D object properties during image generation. This explicit control over 3D structures allows for easy modification of object poses and distances in generated images while automatically obtaining ground-truth 3D annotations. The ability to generate diverse images with specific attributes enhances data augmentation capabilities for training AI models on various tasks like classification and pose estimation. The use of our method significantly improves performance across both in-distribution (ID) datasets like ImageNet-100/200 as well as out-of-distribution (OOD) settings such as ImageNet-R. By pre-training AI models on synthetic data produced using our approach before fine-tuning on target datasets, we observe substantial improvements in accuracy metrics compared to traditional methods without explicit 3D control.

What challenges do diffusion models face without explicit control over the underlying 3D world?

Diffusion models face several challenges when they lack explicit control over the underlying 3D world: Limited Manipulation: Without precise control over 3D structures during image generation, diffusion models struggle to modify object properties like pose and distance accurately. Lack of Ground-Truth Annotations: Difficulty arises in automatically obtaining ground-truth annotations for objects within generated images due to insufficient information about their true spatial configurations. Data Augmentation Limitations: Inadequate diversity hinders effective data augmentation strategies using diffusion models since they cannot produce a wide range of variations required for robust training. Performance Constraints: The inability to explicitly adjust key aspects related to object orientation or position restricts overall model performance across various vision tasks requiring understanding of complex spatial relationships. By addressing these challenges through techniques like ControlNet integration for visual prompting combined with dynamic LLM-generated textual cues within our proposed framework - "Generating Images with 3d Annotations Using Diffusion Models," we overcome limitations faced by traditional diffusion approaches lacking explicit control over the underlying three-dimensional structure during image synthesis processes