VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models

Core Concepts

Utilizing video diffusion models for scalable 3D generative modeling.

Abstract

The paper introduces VFusion3D, a model that leverages pre-trained video diffusion models to generate synthetic multi-view datasets for training feed-forward 3D generative models. By fine-tuning the video diffusion model with 100K 3D data, VFusion3D can reconstruct high-quality 3D assets from single images. The proposed model outperforms current state-of-the-art models and showcases superior performance in user studies. The study also explores the benefits of synthetic multi-view data and scaling trends for large-scale training.

Stats

Trained on nearly 3M synthetic multi-view data.
Users prefer VFusion3D results over 70% of the time.
Fine-tuned with renderings from a 3M synthetic dataset.
Generated a total of 4 million videos for training.
Trained on a total batch size of 1024 with supervision per batch using multi-view images.

Quotes

"Our VFusion3D reconstructs high-quality and 3D-consistent assets from a single input image."
"Users prefer our results over 70% of the time."
"VFusion3D can generate high-quality 3D assets from a single image with any viewing angles."

Key Insights Distilled From

VFusion3D

by Junlin Han,F... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12034.pdf

Deeper Inquiries

How does the scalability of VFusion3D compare to other existing methods in the field?

VFusion3D demonstrates impressive scalability compared to other existing methods in the field of 3D generative models. By utilizing a video diffusion model as a multi-view data generator, VFusion3D can generate an extensive amount of synthetic data for training, enabling scalable learning. The ability to fine-tune the pre-trained video diffusion model with 3D data and then train VFusion3D on this synthetic multi-view dataset allows for efficient scaling. As shown in experiments, increasing the size of the synthetic dataset consistently improves generation quality metrics like LPIPS and CLIP image similarity scores. This scalability is crucial for developing foundation 3D generative models that can efficiently create high-quality 3D assets.

What are the potential limitations of relying on synthetic multi-view data for training?

While relying on synthetic multi-view data offers significant advantages such as scalability and generalization to uncommon objects or scenes, there are also potential limitations associated with this approach:

Limited Real-world Variability: Synthetic datasets may not fully capture all real-world variability present in actual 3D scenes or objects.
Quality vs Quantity Trade-off: Generating large amounts of synthetic data may lead to variations in quality, where some generated samples might lack fidelity or consistency.
Model Generalization: Models trained solely on synthetic data may struggle when faced with real-world scenarios that differ significantly from what was present in the training set.
Specific Object Challenges: Certain types of objects like vehicles or text-related content may pose challenges for fine-tuned video diffusion models, leading to distortions and inconsistencies.

How might advancements in video diffusion models impact the future development of scalable 3D generative models?

Advancements in video diffusion models have significant implications for future developments in scalable 3D generative modeling:

Enhanced Data Generation: Improved video diffusion models can generate more diverse and high-quality multi-view sequences, providing richer training datasets for 3D generative models.
Better Generalization: Advanced video diffusion models could improve model generalization capabilities by capturing intricate details and nuances present within complex scenes or objects.
Efficient Training Processes: With refined techniques and architectures, future video diffusion models could streamline training processes by optimizing convergence rates and reducing computational costs.
Innovative Applications: Progression in video diffusion technology opens up possibilities for innovative applications such as interactive AR/VR experiences, realistic gaming environments, and advanced animation techniques leveraging scalable 3D generative modeling approaches.