GenXD: A Unified Model for Generating 3D and 4D Scenes from Images

Core Concepts

This paper introduces GenXD, a novel framework for generating high-quality, consistent 3D and 4D scenes from single or multiple input images, leveraging a new large-scale 4D dataset called CamVid-30K and innovative multiview-temporal modules to disentangle camera and object motion.

Abstract

Bibliographic Information:

Zhao, Y., Lin, C., Lin, K., Yan, Z., Li, L., Yang, Z., Wang, J., Lee, G.H., & Wang, L. (2025). GenXD: Generating Any 3D and 4D Scenes. In Proceedings of the International Conference on Learning Representations (ICLR 2025).

Research Objective:

This paper aims to address the challenges of 3D and 4D scene generation, particularly the lack of large-scale, diverse 4D datasets and the need for effective models that can handle both static and dynamic scenes with varying input views.

Methodology:

The authors introduce a data curation pipeline to create CamVid-30K, a large-scale 4D dataset with camera pose and object motion annotations derived from existing video datasets. They propose GenXD, a unified framework based on latent diffusion models, incorporating multiview-temporal modules to disentangle camera and object motion and masked latent conditioning to support single and multi-view image inputs. GenXD is trained on a combination of 3D and 4D datasets and evaluated on various tasks, including 4D scene and object generation, and few-view 3D reconstruction.

Key Findings:

• The proposed data curation pipeline effectively extracts camera poses and object motion information from videos, enabling the creation of a large-scale 4D dataset.
• GenXD demonstrates superior performance in generating high-quality, consistent 3D and 4D scenes compared to existing methods, achieving state-of-the-art results in various benchmarks.
• The multiview-temporal modules effectively disentangle camera and object motion, leading to improved consistency and controllability in generated scenes.
• Masked latent conditioning allows GenXD to handle single and multi-view image inputs seamlessly, enhancing its versatility for different applications.

Main Conclusions:

This research significantly contributes to the field of 3D and 4D scene generation by introducing a novel framework, GenXD, and a large-scale 4D dataset, CamVid-30K. GenXD's ability to generate high-quality, consistent scenes from various input views opens up new possibilities for applications in gaming, visual effects, and virtual reality.

Significance:

This work addresses a critical gap in 3D and 4D content creation by providing a robust and versatile solution for generating realistic scenes from images. The introduction of CamVid-30K further facilitates research and development in this rapidly evolving field.

Limitations and Future Research:

The paper acknowledges the computational demands of training and deploying such models. Future research could explore more efficient architectures and training strategies. Additionally, investigating the generation of higher-resolution scenes and incorporating semantic understanding for finer control over scene elements are promising directions.

Stats

CamVid-30K contains approximately 30,000 4D data samples.
GenXD is trained on 32 A100 GPUs with a batch size of 128 and a resolution of 256x256.
GenXD outperforms CameraCtrl and MotionCtrl in FID and FVD metrics for 4D scene generation.
GenXD is 100x faster than Animate124 in 4D object generation.
Using GenXD improves PSNR on Re10K and LLFF by 4.82 and 5.13, respectively, for few-view 3D reconstruction.

Quotes

"In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life."
"The first and foremost challenge in 4D generation is the lack of general 4D data."
"GenXD can generate videos that follow the camera trajectory as well as consistent 3D views that can be lifted into 3D representations."

Key Insights Distilled From

GenXD: Generating Any 3D and 4D Scenes

by Yuyang Zhao,... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.02319.pdf

Deeper Inquiries

How can GenXD be extended to incorporate user interaction, allowing for real-time manipulation and editing of generated 3D and 4D scenes?

GenXD, as a powerful 3D and 4D scene generation framework, can be extended to facilitate real-time user interaction and scene manipulation in several ways:
1. Incorporating 3D-Aware Editing Tools:

Direct Manipulation in 3D Space: By leveraging the underlying 3D representations (3D Gaussian Splatting or Zip-NeRF) used by GenXD, users could directly manipulate objects or elements within the generated 3D scene. This could involve tools for:

Translation, Rotation, Scaling: Allowing users to reposition, resize, or reorient objects in the scene.
Deformation: Enabling users to modify the shape of objects using control points or sculpting tools.

Parametric Control: Introducing sliders or input fields that map to specific parameters of the generative model. This would allow users to adjust:

Object Attributes: Changing color, texture, material, or lighting properties of objects.
Scene Parameters: Modifying the overall lighting, camera position, or background environment.
2. Leveraging Image-Based Editing with In-Situ Refinement:

Image Inpainting/Editing: Users could edit a generated 2D view of the scene using standard image editing tools. GenXD could then be used to re-synthesize the 3D scene, incorporating the user's edits while maintaining consistency across viewpoints.
Sketch-Based Input: Users could provide rough sketches or drawings on top of a generated view to indicate desired modifications. GenXD could interpret these sketches and refine the 3D scene accordingly.
3. Utilizing Conditional Generation and Latent Space Exploration:

Conditional Input: Allowing users to provide additional image or text prompts to guide the generation process. For example, a user could provide an image of a chair and ask GenXD to add a similar chair to the scene.
Latent Space Manipulation: Providing tools for users to explore the latent space of the generative model. This could enable the discovery of variations or interpolations of the generated scene, allowing for more creative exploration.
Challenges and Considerations:

Real-time Performance: Achieving real-time interaction and manipulation would require optimizing the underlying 3D representations and the generative model for speed and efficiency.
User Interface Design: Developing an intuitive and user-friendly interface for interacting with and editing 3D and 4D scenes is crucial.
Consistency and Coherence: Ensuring that user edits are seamlessly integrated into the generated scene while maintaining 3D consistency and temporal coherence (for 4D) is essential.

Could the reliance on large datasets and computationally intensive training limit the accessibility and practical application of GenXD for users with limited resources?

Yes, the reliance on large datasets and computationally intensive training poses significant challenges to the accessibility and practical application of GenXD for users with limited resources:
1. Dataset Requirements:

Data Acquisition: Obtaining large-scale, high-quality 3D and 4D datasets, especially for specialized domains, can be prohibitively expensive and time-consuming.
Storage and Processing: Storing and processing massive datasets requires significant storage capacity and computational power, which may not be available to users with limited resources.
2. Computational Demands:

Training: Training GenXD on large datasets demands substantial computational resources, typically requiring powerful GPUs and extended training times.
Inference: Even with a trained model, generating high-resolution 3D and 4D content can still be computationally demanding, potentially limiting real-time applications.
Potential Solutions and Mitigations:

Model Compression and Optimization: Techniques like model pruning, quantization, and knowledge distillation can be explored to reduce the size and computational requirements of GenXD.
Cloud-Based Services: Cloud computing platforms can provide access to powerful GPUs and storage infrastructure, making GenXD more accessible to users without dedicated hardware.
Transfer Learning and Fine-tuning: Pre-trained GenXD models could be fine-tuned on smaller, domain-specific datasets, reducing the need for extensive training from scratch.
Open-Source Initiatives and Collaboration: Open-sourcing pre-trained models and datasets can foster collaboration and make GenXD more accessible to a wider community.
Balancing Innovation and Accessibility:
It's crucial to strike a balance between developing cutting-edge generative models like GenXD and ensuring their accessibility to a broad range of users. Exploring the solutions mentioned above will be essential for democratizing access to these powerful technologies.

What are the potential implications of generating increasingly realistic 3D and 4D content on our perception of reality and the ethical considerations surrounding its use?

The ability to generate increasingly realistic 3D and 4D content using models like GenXD raises profound implications for our perception of reality and necessitates careful consideration of the ethical challenges:
Impact on Perception of Reality:

Blurring the Lines: Hyperrealistic 3D and 4D content can blur the lines between the real and the virtual, making it increasingly difficult to distinguish between authentic and synthetic media.
Erosion of Trust: The proliferation of fabricated content could erode trust in visual media, making it challenging to discern truth from falsehood.
Manipulation and Deception: The potential for malicious actors to create and spread convincing deepfakes, manipulating events or impersonating individuals, poses significant risks.
Ethical Considerations:

Misinformation and Disinformation: The ease of generating realistic but fabricated content amplifies the dangers of spreading misinformation and manipulating public opinion.
Privacy Violations: Creating synthetic 3D models of individuals without their consent or using their likeness in fabricated scenarios raises serious privacy concerns.
Bias and Representation: Generative models trained on biased datasets can perpetuate and amplify existing societal biases, leading to unfair or discriminatory outcomes.
Authenticity and Attribution: Establishing clear mechanisms for attributing authorship and verifying the authenticity of 3D and 4D content is crucial to combat misinformation.
Mitigating Ethical Risks:

Developing Detection Tools: Investing in research and development of robust detection techniques to identify synthetic content is essential.
Promoting Media Literacy: Educating the public about the capabilities and limitations of generative models can empower individuals to critically evaluate visual media.
Establishing Ethical Guidelines and Regulations: Developing clear ethical guidelines and regulations for the creation and dissemination of synthetic content is crucial.
Fostering Responsible Innovation: Encouraging responsible innovation by promoting transparency, accountability, and ethical considerations in the development and deployment of generative models.
Balancing Innovation and Responsibility:
As we enter an era where the line between the real and the synthetic becomes increasingly blurred, it is paramount to address these ethical challenges proactively. Striking a balance between fostering innovation and ensuring the responsible use of these powerful technologies is crucial for a future where trust in information and respect for individual rights are preserved.