RI Seminar: Jia Deng : Toward an ImageNet Moment for Synthetic Data
TLDRThe speaker discusses the project 'Infin Gen', a procedural generator for infinite synthetic data, emphasizing its potential in computer vision tasks. The system, built on decades of computer graphics research and Blender's open-source toolkit, creates 3D scenes and objects with high-quality labels, offering customization and control over the data distribution. The speaker highlights the challenges in generating realistic 3D vision data and how Infin Gen addresses them by providing detailed, editable scenes. The project's roadmap includes expanding to indoor scenes and improving usability, with the ultimate goal of enhancing the training of computer vision models.
Takeaways
- 🌟 The speaker discusses the ImageNet moment in computer vision, highlighting its impact on object detection but noting its limited application to other areas like 3D vision due to the lack of high-quality labeled data.
- 🚀 The presentation introduces 'Infinity Genen', a project for generating infinite synthetic data for computer vision tasks, emphasizing its procedural nature and the use of mathematical rules rather than AI-generated data.
- 🌐 'Infinity Genen' is not a static dataset but a generator that can produce 3D scenes and render images from them, offering random seeds and user controls for variability.
- 🎨 The system is built on decades of computer graphics research and the Blender open-source graphics kit, leveraging procedural generation for shapes, textures, materials, and more.
- 🔄 The talk addresses the domain gap issue, explaining that while synthetic datasets have limitations, photorealism can be achieved with enough resources, and the goal is to expand the coverage and detail of synthetic data.
- 📈 The speaker shares examples of procedural generation, such as creating trees with controllable parameters and random variables, showcasing the system's flexibility and potential for customization.
- 🌲 'Infinity Genen' covers a lot of the natural world, including underwater objects, plants, trees, and animals, with a focus on compositional structure to allow for exponential growth in diversity.
- 🏠 The roadmap for 'Infinity Genen' includes expanding to indoor scenes and urban environments, with the potential to integrate with large language models for natural language prompts to generate 3D scenes.
- 💡 The procedural nature of 'Infinity Genen' allows for high-quality labels and customization, with the possibility of optimizing the data generation process and verifying data security.
- 🔧 The development of 'Infinity Genen' involves a combination of algorithms, math, and art, leading to interesting side projects and a better appreciation of the visual world.
- 📚 The speaker concludes by acknowledging the team behind 'Infinity Genen' and inviting questions, emphasizing the project's potential for growth and its current status as a valuable tool for computer vision research.
Q & A
What is the main focus of the talk?
-The main focus of the talk is the development and application of synthetic data for computer vision, specifically addressing the limitations in 3D vision and exploring the potential of procedural generation in creating infinite synthetic data.
Why has the 'ImageNet moment' not happened for all of computer vision?
-The 'ImageNet moment' has not happened for all of computer vision because it has only occurred in a subset of computer vision around object detection, where there is an abundance of high-quality labeled data. For other areas, such as 3D vision, the essential ingredients of high-quality labeled data, powerful computing resources, and effective algorithms are not present.
What are the challenges in collecting high-quality 3D labeled data?
-Collecting high-quality 3D labeled data is challenging because depth sensors used for data collection have limited range and resolution, they don't work on all surfaces, and the process is tedious and resource-intensive.
How does synthetic data generated by conventional computer graphics differ from that generated by AI models?
-Synthetic data generated by conventional computer graphics is created using mathematical rules and procedural generation, allowing for unlimited quantities and automatic high-quality labels. In contrast, synthetic data generated by AI models often involves creating new data from existing data, which may not provide the same level of control or diversity.
What is the significance of the procedural generator 'Infin Gen' presented in the talk?
-Infin Gen is a generator of infinite synthetic data that is 100% procedural, meaning everything from shape, texture, material, lighting, scene arrangement, and animation is generated from scratch using randomized mathematical rules. This provides a flexible and controllable way to generate synthetic data for various applications.
How does the compositional nature of the world affect the diversity of procedurally generated data?
-The compositional nature of the world allows for exponential growth in the diversity of procedurally generated data. As more generators are developed, they can be combined in various ways to create new variations, leading to a vast array of possible scenes and objects.
What are the benefits of using real geometry in the synthetic data generation process?
-Using real geometry ensures that the 3D ground truth data is accurate, which is crucial for training reliable computer vision systems. It also allows for adaptive resolution scaling to optimize efficiency and provides a more realistic representation of the objects and scenes.
How does the open-source nature of Infin Gen impact its usability?
-The open-source nature of Infin Gen means that it is freely available for anyone to use, allowing for unlimited generation of not just images but also 3D models. Its modular and customizable codebase enables users to control and optimize the data generation process, verify security, and create adversarial test cases.
What are the current limitations of Infin Gen in terms of data coverage and information?
-Currently, Infin Gen has less coverage than datasets like ImageNet or COCO but provides a lot more information for each image, including detailed annotations and high-quality labels. The system is also focused on natural objects and scenes, with plans to expand to include urban environments and other types of data in the future.
What are the potential applications of Infin Gen in the field of computer vision and beyond?
-Infin Gen can be used for training advanced computer vision systems, particularly in areas such as 3D vision where high-quality labeled data is scarce. It can also be applied in domains like robotics, virtual reality, and game development, where photorealistic and controllable synthetic data is valuable.
How does the talk address the issue of distribution in synthetic data generation?
-The talk suggests that while it's beneficial for the synthetic data distribution to be close to the real world, it's not necessary to be fully faithful. The visual system can handle a larger distribution than what exists in reality, and domain randomization introduced through synthetic data can be beneficial for system robustness.
Outlines
🎤 Introduction and Context
The speaker begins with an acknowledgment of the generous introduction by Jan and expresses a concern about living up to it. They note their excitement about being at CMU and sharing newer work, particularly since it's been a while since their last visit. The speaker intends to discuss their work on an image for synthetic data, highlighting the significance of having 'image' in the title. They reflect on their past experiences with faculty jobs and the evolution of computer vision, particularly focusing on object detection and the success achieved in this area due to the availability of high-quality labeled data. However, they point out that this 'image moment' has not been universal across all of computer vision, especially in 3D vision, due to the lack of essential ingredients such as high-quality labeled data.
🚀 The Potential of Synthetic Data
The speaker delves into the limitations of 3D vision, such as the difficulty in obtaining depth values for pixels from a single image. They discuss the challenges with current methods, including the use of depth sensors with limited range and resolution. The speaker then introduces synthetic data as a promising alternative, clarifying that they refer to data generated by conventional computer graphics, not AI models. They emphasize the benefits of such synthetic data, including unlimited quantity and automatic high-quality labels. The speaker also addresses concerns about the domain gap between synthetic and real data, arguing that photorealism is achievable with enough resources, and that existing synthetic data sets have limitations in detail, realism, and coverage of the real world.
🌐 Introducing Infin Gen: Infinite Synthetic Data Generator
The speaker presents their project, Infin Gen, a generator of infinite synthetic data. They clarify that it is not a static dataset but a dynamic generator that produces 3D scenes and renders images from them, using random seeds and user controls as inputs. Infin Gen is 100% procedural, meaning everything from shapes to materials, lighting, and animations is generated from scratch using randomized mathematical rules. The speaker highlights the benefits of this approach, including the ability to generate unlimited quantities of data and the flexibility to customize distributions for specific applications.
🌳 Procedural Generation of Natural Objects
The speaker elaborates on the procedural generation of natural objects in Infin Gen, explaining how it works and the components it includes. They discuss the creation of a tree generator as an example, detailing the rules for branching, materials, leaves, and fruits. The speaker emphasizes the high-level controllable parameters for users and the low-level random variables that make each tree unique. They also mention the use of the Blender open-source graphics kit and the significance of the Blender node system in enabling this project, allowing artists to compose mathematical functions to determine the shape and material of objects.
🎥 Animation and Simulation in Infin Gen
The speaker discusses the capabilities of Infin Gen in creating animations and simulations. They mention the procedural generation of clouds, the rigging system for animals, and the terrain system that includes various landscapes. The speaker explains how procedural noise is used to generate these scenes and how marching cubes are employed to convert them into meshes. They also touch on material generators and the procedural composition system that allows for the creation of complex scenes with detailed assets and photorealistic rendering.
📈 The Compositionality and Diversity of Infin Gen
The speaker explores the concept of compositionality in Infin Gen, explaining how it allows for the reuse of subcomponents across different generators, leading to exponential growth in diversity. They argue that the world is compositional and that by developing generators for different categories of objects, one can cover a wide range of the real world. The speaker addresses the concern of matching the diversity of the real world, stating that while the real world is infinitely complex, the compositional nature of Infin Gen allows for a system that can generate a diverse range of objects and scenes.
🏙️ Expanding Infin Gen to Urban Environments
The speaker acknowledges questions about the applicability of Infin Gen to urban environments, noting that while it is not currently available, it is on the roadmap. They discuss the potential for expanding the system's coverage and the challenges of creating structured indoor scenes compared to natural, unstructured environments. The speaker introduces a constraint system that allows users to specify the number and arrangement of objects in a scene, and a solver that attempts to satisfy these constraints. They also mention the development of a layout system for generating room layouts and placing objects within them according to the specified constraints.
🌟 The Future of Infin Gen and Its Impact
The speaker shares their vision for the future of Infin Gen, including plans to expand its coverage and improve usability for various applications. They mention the development of a real-time simulator for unbounded scenes and the potential for robotics applications. The speaker also discusses the educational aspect of Infin Gen, as it combines algorithms, math, and art, providing a deeper appreciation of the visual world. They acknowledge the contributions of their students and the ongoing development of the system, emphasizing the importance of community involvement and open-source contributions.
💬 Addressing Questions and Concerns
The speaker addresses questions about the distribution of images generated by Infin Gen and its ability to match real-world complexity. They clarify that while the current system does not claim to fully generate any possible real-world image, it is engineered to have the potential to do so. The speaker also discusses the possibility of connecting Infin Gen to existing real-world data, such as spatial configurations from datasets like COCO, to create scenes that reflect real-world distributions. They highlight the procedural nature of Infin Gen, allowing users to customize distributions and generate specific objects or scenes as needed.
Mindmap
Keywords
💡Image Moment
💡Synthetic Data
💡3D Vision
💡Procedural Generation
💡Blender
💡Node Graphs
💡Compositionality
💡Distribution
💡Real Geometry
💡Open Source
💡Cost of Generation
Highlights
The speaker discusses the limitations of the 'ImageNet moment' in computer vision, particularly in areas outside of object detection like 3D vision.
The lack of high-quality labeled data for 3D vision tasks is highlighted as a major obstacle in the field.
Depth sensors like Kinect and LiDAR are mentioned as current methods for collecting 3D data, but their limitations are discussed.
The potential of synthetic data for training computer vision models is emphasized, especially when generated through conventional computer graphics.
Synthetic data generated by AI models may not be as effective due to a potential circular dependency in training AI on data it generates itself.
The speaker introduces 'Infinity Genen', a generator of infinite synthetic data that is 100% procedural and symbolic.
Infinity Genen is described as a generator, not a static dataset, allowing for random seeds and user controls for 3D scene generation.
The procedural nature of Infinity Genen is explained, meaning everything from shape to texture and animation is generated using mathematical rules.
The project is built on decades of computer graphics research and the Blender open-source graphics kit, particularly leveraging Blender's geometry nodes introduced in 2021.
A node transpiler is developed to convert node graphs into Python code, enhancing expressiveness and programmer-friendliness.
The speaker addresses the domain gap between synthetic data and real-world data, emphasizing that photorealism is achievable with enough resources.
The compositional nature of the world is discussed, explaining how reusing subcomponents can lead to exponential growth in diversity for synthetic data generation.
The speaker argues that the distribution of synthetic data doesn't need to be fully faithful to the real world; it can be an approximate envelope which can still be beneficial.
The practical applications of Infinity Genen are mentioned, including agriculture and ecological applications, as well as training advanced vision systems.
The procedural system allows for high-quality labels and customizable distribution, making it valuable for training computer vision models on downstream tasks.
The speaker shares a roadmap for future work, including expanding Infinity Genen's coverage and improving usability for different use cases.
A constraint system is developed to help place objects in indoor scenes in a structured way, satisfying user-specified constraints.
The speaker concludes by acknowledging the students and team behind the development of Infinity Genen and invites questions for further discussion.