C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds (1912.07009v2)

Abstract: Flow-based generative models have highly desirable properties like exact log-likelihood evaluation and exact latent-variable inference, however they are still in their infancy and have not received as much attention as alternative generative models. In this paper, we introduce C-Flow, a novel conditioning scheme that brings normalizing flows to an entirely new scenario with great possibilities for multi-modal data modeling. C-Flow is based on a parallel sequence of invertible mappings in which a source flow guides the target flow at every step, enabling fine-grained control over the generation process. We also devise a new strategy to model unordered 3D point clouds that, in combination with the conditioning scheme, makes it possible to address 3D reconstruction from a single image and its inverse problem of rendering an image given a point cloud. We demonstrate our conditioning method to be very adaptable, being also applicable to image manipulation, style transfer and multi-modal image-to-image mapping in a diversity of domains, including RGB images, segmentation maps, and edge masks.

• The paper introduces C-Flow, a novel framework for conditional generative flow models that enables data generation across image and 3D point cloud domains.
• C-Flow employs conditional coupling layers and explores methods like Hilbert curves for handling unordered 3D point clouds, demonstrating practical solutions for 2D-to-3D conversion.
• Experimental results show competitive performance on tasks like 3D reconstruction and image translation, broadening the application scope of flow-based models.
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Conditional Generative Flow Models for Images and 3D Point Clouds

The paper introduces a novel approach to generative models through a framework called C-Flow, which leverages flow-based generative models for conditional data-generation across multiple domains. Flow-based models are known for properties like exact log-likelihood computation and an invertible mapping between data and latent space, but they remain less explored compared to VAEs and GANs. This research endeavors to address several multi-modal data modeling tasks including image-to-image translation, style transfer, 3D reconstruction from images, and rendering images based on 3D point clouds.

C-Flow: A Scheme for Flow Conditioning

The primary contribution of the paper is a conditioning scheme designed for normalizing flows to facilitate multi-modal transfer tasks. C-Flow implements two parallel flow branches interconnected through conditional coupling layers. This configuration enables a source domain to influence a target domain, preserving the inherent characteristics of flow-based models such as efficient synthesis and exact inference within the latent space.

Key Methodologies:

1. Conditional Coupling Layers: These innovative layers allow for conditioning data generation across different domains while maintaining invertibility, facilitating fine-grained control over the generative process.
2. Cycle Consistency: Introduces an invertible cycle consistency loss that aims to stabilize training by ensuring similarity between generated samples and original data points.

Modeling Unordered 3D Point Clouds

One of the most notable achievements of C-Flow is its applicability to unordered 3D point clouds, a challenging task given their lack of spatial ordering. The authors propose:

• Hilbert Space-Filling Curves: As a method to reorder point clouds, enabling convolutional operations by establishing a spatial neighborhood.
• Global Feature Approximation: Incorporates an invertible operation in the conditional scheme to adapt global 3D data features, crucial for maintaining point cloud integrity.
• Chamfer Distance for Consistency: Utilizing Chamfer distance for cycle consistency loss to ensure robust handling of point cloud data.

Experimental Evaluations and Implications

The paper reports tests on ShapeNet for 3D point clouds as well as multi-domain datasets for image-to-image translation. Quantitative measures demonstrate competitive performance in terms of Chamfer distance for 3D modeling and improved metrics like Structural Similarity Index Measure (SSIM) and Inception Score for image tasks, affirming the adaptability and efficacy of C-Flow.

Implications:

• Generative Models: C-Flow broadens the scope of applications for flow-based models, showcasing their potential in tasks traditionally dominated by adversarial networks.
• 3D Reconstruction and Rendering: Offers practical solutions in domains requiring conversion between 2D and 3D data, with potential impacts on areas like computer graphics and augmented reality.
• Future Research Directions: Encourages further exploration into the conditioning of normalizing flows for diverse applications, increasing robustness through cycle consistency strategies.

The capabilities presented in the paper underscore the importance of innovative conditioning in generative modeling, paving the way for techniques that can seamlessly transition across different data modalities. While some evaluations yield domain-specific results, the generalizability across varied tasks heralds substantial promise for future developments in generative modeling with flow-based techniques.