Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings (2411.08017v1)

Abstract: Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute this limitation to the inefficiency of current representations, which lack the compactness required to model the generative models effectively. To address this, we introduce a novel approach called Wavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based, compact latent encodings. Specifically, we compress a $256^3$ signed distance field into a $12^3 \times 4$ latent grid, achieving an impressive 2427x compression ratio with minimal loss of detail. This high level of compression allows our method to efficiently train large-scale generative networks without increasing the inference time. Our models, both conditional and unconditional, contain approximately one billion parameters and successfully generate high-quality 3D shapes at $256^3$ resolution. Moreover, WaLa offers rapid inference, producing shapes within two to four seconds depending on the condition, despite the model's scale. We demonstrate state-of-the-art performance across multiple datasets, with significant improvements in generation quality, diversity, and computational efficiency. We open-source our code and, to the best of our knowledge, release the largest pretrained 3D generative models across different modalities.

• The paper presents a novel wavelet-based encoding that compresses 3D models by 2,427× while preserving key details.
• It achieves state-of-the-art 3D shape generation at 256³ resolution in just two to four seconds across varied datasets.
• It offers open-source tools for both conditional and unconditional generation, enhancing reproducibility and versatility.

A Technical Overview of Wavelet Latent Diffusion (WaLa)

The paper introduces Wavelet Latent Diffusion (WaLa), an innovative approach in the domain of 3D generative modeling. Responding to the challenges associated with representing high-resolution 3D shapes, WaLa employs a wavelet-based encoding scheme to significantly compress 3D representations while retaining essential details. This approach facilitates the efficient training of large-scale diffusion-based generative models, which consist of approximately one billion parameters, on highly compact latent representations without prolonging inference times.

Main Contributions

• Compact Encoding with Wavelets: WaLa leverages wavelet transformations to convert 3D models into highly compressed latent representations. Specifically, it manages to compress a $256^3$ signed distance field into a $12^3 \times 4$ latent grid—achieving a 2,427× compression ratio. This compact representation is critical in ensuring models can be managed effectively without loss of significant detail.
• High-Quality 3D Shape Generation: The methodological approach yields models capable of generating detailed 3D shapes at $256^3$ resolution rapidly, typically within two to four seconds—a strong performance given the scale of the models. The paper reports state-of-the-art results across multiple datasets in terms of both diversity and quality of the generated shapes.
• Versatile Conditioning: WaLa supports both conditional and unconditional models. It allows for shape generation from various inputs, including sketches, text, images, low-resolution voxels, point clouds, and depth maps. This capability underscores the method's flexibility and adaptability across different 3D modeling tasks.
• Open Source and Broad Applicability: To promote further research and reproducibility, the authors have open-sourced their code, marking the release of what they believe is the largest pretrained 3D generative model to date. This model is versatile across different input modalities.

Theoretical and Practical Implications

The introduction of WaLa signifies a meaningful progression in how 3D data is represented and manipulated in generative frameworks. The compactness of wavelet encodings challenges existing paradigms by balancing the trade-offs between compressibility and the fidelity of 3D representations. From a theoretical standpoint, WaLa offers a new lens through which the efficiency of 3D generative models can be enhanced, potentially influencing future architectures to favor wavelet-based encodings.

Practically, WaLa enables high-capacity models to be more accessible for tasks ranging from industrial design to entertainment due to its efficient training and inference capabilities. This is particularly relevant as the demand for detailed and rapid 3D generation grows in areas such as virtual reality, gaming, and automated design.

Future Directions

The possibilities for extending WaLa's application are numerous, including personalized generative models that could generate items aligned with user preferences or specific domain requirements, informed by ongoing advancements in conditional modeling. Moreover, the integration of WaLa into multi-modal AI systems could facilitate seamless transitions and interactions between 3D objects and other types of data, such as audio or complex procedural rules. Additionally, adapting WaLa to handle dynamic 3D data could unlock further potential in animating complex scenes and characters in real-time applications.

In summary, WaLa represents a significant technical achievement in the field of 3D generative modeling. By successfully addressing the challenges of compact representation and efficient high-resolution shape generation, it sets a new standard for future research and applications. The work opens strategic pathways for leveraging wavelet-based encodings in complex learning tasks, poised to influence both academic and commercial sectors substantially.