Orchid: Image Latent Diffusion for Joint Appearance and Geometry Generation (2501.13087v1)

Abstract: Diffusion models are state-of-the-art for image generation. Trained on large datasets, they capture expressive image priors that have been used for tasks like inpainting, depth, and (surface) normal prediction. However, these models are typically trained for one specific task, e.g., a separate model for each of color, depth, and normal prediction. Such models do not leverage the intrinsic correlation between appearance and geometry, often leading to inconsistent predictions. In this paper, we propose using a novel image diffusion prior that jointly encodes appearance and geometry. We introduce a diffusion model Orchid, comprising a Variational Autoencoder (VAE) to encode color, depth, and surface normals to a latent space, and a Latent Diffusion Model (LDM) for generating these joint latents. Orchid directly generates photo-realistic color images, relative depth, and surface normals from user-provided text, and can be used to create image-aligned partial 3D scenes seamlessly. It can also perform image-conditioned tasks like joint monocular depth and normal prediction and is competitive in accuracy to state-of-the-art methods designed for those tasks alone. Lastly, our model learns a joint prior that can be used zero-shot as a regularizer for many inverse problems that entangle appearance and geometry. For example, we demonstrate its effectiveness in color-depth-normal inpainting, showcasing its applicability to problems in 3D generation from sparse views.

• The paper introduces a unified latent diffusion model that jointly encodes color, depth, and surface normals into a single latent space.
• It uses a two-step approach combining a VAE and a text-conditioned latent diffusion process to ensure visual and geometric coherence.
• The framework enables effective cross-modal inpainting and unified 3D scene generation, with potential applications in VR, robotics, and autonomous systems.

Overview of Orchid: Image Latent Diffusion for Joint Appearance and Geometry Generation

The paper introduces "Orchid," a unified latent diffusion model that simultaneously generates color, depth, and surface normals from text and images. This model addresses the typical challenge in image generation tasks where appearance, such as color, and geometric properties, like depth and surface normals, are often dissociated. By training a Variational Autoencoder (VAE) that incorporates these three attributes together in a joint latent space, Orchid enables the generation of visually coherent and geometrically accurate representations.

The Orchid model utilizes a two-step approach: a VAE to encode the data into a latent space and a Latent Diffusion Model (LDM) to generate the joint latents. Unlike conventional models requiring separate networks for each prediction type, Orchid's single model can synthesize these elements cohesively, ensuring internal consistency among appearance and geometry.

Methodology and Features

Key features of Orchid include:

• Joint Latent Space: By extending the conventional image latent diffusion space to include depth and surface normals, Orchid leverages the intrinsic correlation between these different modalities. This design ensures that generated outputs maintain congruence between visual and geometric aspects.
• Diffusion Training: Orchid employs a text-conditioned LDM to guide the generation process in the joint latent space. The training incorporates a large multifaceted dataset encompassing real and synthetic data to achieve robust zero-shot performance.
• Color-Conditioned Depth and Normal Generation: Beyond text-only generation, the model is fine-tuned for color-conditioned settings without an external text prompt, aligning its outcomes closely with state-of-the-art metrics in monocular depth and surface normal prediction.

Contributions and Implications

The Orchid framework demonstrates several applications that capitalize on its joint latent space methodologies:

1. Cross-Modal Inpainting: With a foundation built upon joint prior learning, Orchid is adept at inpainting missing visual data across its three modalities effectively, demonstrating utility in inverse problems where the visual appearance is intertwined with spatial geometry.
2. Unified 3D Scene Generation: Orchid's ability to construct partial 3D scenes from text or images exhibits potential for virtual reality and robotics interactive environments. It efficiently combines learned image and geometric priors, simplifying traditional cascades of separate generation models.
3. Enhanced Consistency: Unlike preceding models where sequential tasks might introduce compounded errors, Orchid maintains high internal consistency between predicted depths and normals, as empirically validated through quantitative assessments.

Future Prospects

The potential applications for Orchid are vast. The model's capability to seamlessly integrate and generate 3D content from limited input data suggests significant advancements in fields such as autonomous driving simulations, augmented reality, and single-view 3D reconstruction. The authors highlight the model's capacity to serve as a 2.5D foundation that could be extended to other modalities and domains.

Currently, one limitation of the Orchid model is its dependence on the intricate pretraining of its VAE component, which should be tackled with access to broader and more diverse datasets over time. The advancements in hardware and distributed computing environments could also facilitate these expansions in model capability.

This research represents a noteworthy stride in joint image and depth synthesis, offering a robust framework for combined appearance and geometry prediction tasks that were traditionally handled separately, thus heralding potential evolution in multidimensional content generation.