LDM3D-VR: Advancing Virtual Reality with Latent Diffusion Models

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Nov 9 2023

In a recent submission to the arXiv* server, researchers introduced the Latent Diffusion Model for 3D virtual reality (LDM3D-VR), comprising the LDM3D-pano and the super-resolution (SR)-based model LDM3D-SR for VR applications.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

Background

Diffusion models have revolutionized content creation, enabling the generation of RGB (Red, Green, and Blue) images from text prompts and enhancing low-resolution inputs into high-resolution RGB images. However, a persistent challenge is creating depth maps simultaneously with RGB images, which is crucial for VR content development. Conventional image stitching methods often suffer from artifacts and irregular shapes.

Text-to-panorama, translating textual inputs into panoramic images, is essential for VR environment creation. Early approaches used Generative Adversarial Networks (GANs), but recent diffusion models offer improved training stability and generalization. Some methods only focus on horizontal rotations, neglecting vertical perspectives, while others struggle due to limited training data. LDM3D-pano addresses these challenges by creating realistic RGB panoramas and depth maps from textual prompts. In image SR, convolutional neural networks (CNNs) were initially used, and GANs improved fidelity.

Recent advancements employ attention mechanisms and transformer-based architectures. Denoising diffusion probabilistic models excel in image generation and upscaling. Enhancing depth map resolution is another explored area. The diffusion-based super-resolution model, LDM3D-SR, addresses this need by increasing the resolution of RGBD (Red, Green, Blue, and Depth) within a unified architecture. These innovations mark significant progress in the field of computer vision, benefiting content creation, VR, and image enhancement.

LDM3D-VR framework

LDM3D-pano, an extension of the LDM3D framework, focuses on panoramic image generation. Key adaptations involve the customization of the KL-autoencoder's first and last Conv2d layers to handle a 4-channel input, combining RGB with a depth map (LDM3D-4c). The diffusion model, employing a latent space U-Net, incorporates a Contrastive Language-Image Pre-training (CLIP) text encoder for text-based conditioning through a cross-attention mechanism.

The fine-tuning process involves two stages. Initially, the refined KL-autoencoder in LDM3D-4c undergoes fine-tuning using image, and text pair samples from dataset LAION-400M, with depth map labels from a depth map transformer model (DPT-BEiT-L-512). Subsequently, the U-Net backbone fine-tunes based on Stable Diffusion (SD) v1.5, utilizing a subset of LAION Aesthetics 6+ with nearly 20,000 tuples, including captions, 512x512-sized images, and depth maps from DPT-BEiT-L-512. Further fine-tuning occurs on a panoramic dataset, originally comprising High Dynamic Range (HDR) images augmented into panoramic images of size 512x1024, producing training and validation images.

By comparison, LDM3D-SR encodes low-resolution (LR) images into a latent space using the KL-Autoencoder from LDM3D-4c. The previously discussed U-Net components are still included in the diffusion model, which now accepts an eight-channel input to enable LR conditioning by concatenation with noise during inference and high-resolution (HR) latent during training. Cross-attention using a CLIP text encoder facilitates text conditioning. The U-Net in LDM3D-SR is fine-tuned based on the SD-superres model, utilizing the images from LR and HR sets. Different methods for generating LR depth maps, including depth estimation, HR depth map conditioning, and bicubic degradation, are explored.

Evaluation of the prowess of LDM3D-pano and LDM3D-SR

Panoramic RGBD Generation: In the assessment of text-to-panorama RGBD generation, the authors utilized the validation set from their dataset. The evaluation encompassed both image quality and depth analysis. In the context of image quality assessment, LDM3D-pano was compared to Text2light LDR, a model known for text-driven Low Dynamic Range (LDR) panorama creation. Evaluation metrics included Inception Score (IS), Frechet Inception Distance (FID), and CLIP similarity. LDM3D-pano achieved a higher FID score, suggesting a deficiency in local awareness and patch-based semantic coherence. However, it excelled in IS and CLIPsim scores, demonstrating its ability to generate diverse images.

For depth evaluation, LDM3D-pano was compared to a baseline model for depth estimation, Joint_3D60_Fres. The mean absolute relative error (MARE) was computed, with LDM3D-pano outperforming the baseline in both standard and outlier-excluded MARE measurements.

HR-RGBD Generation: In the evaluation of HR-RGBD generation, a subset from ImageNet-Val was employed. The evaluation considered image quality using metrics such as IS, reconstruction FID, structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). LDM3D-SR was compared to various models, revealing that it achieved the highest FID and the second-highest IS. In contrast, bicubic regression scored highest in SSIM and PSNR, although those metrics tend to prioritize blurriness over high-frequency details.

An ablation study was conducted for different depth preprocessing methods in LDM3D-SR, with results favoring the use of bicubic degradation of the initial depth map. Depth evaluation closely mirrored the methodology used for LDM3D-pano, with LDM3D-SR exhibiting superior performance compared to bicubic regression. Depth maps generated by LDM3D-SR displayed high-resolution features in both images and depth maps.

Conclusion

In summary, researchers introduced LDM3D-SR and LDM3D-pano for 3D VR developments. LDM3D-pano excels in generating high-quality, diverse panoramic images with panoramic depth, while LDM3D-SR upscaling RGBD images surpasses related upscaling methods and generates high-resolution depth maps. Future research may explore combining these domains for enhanced high-resolution panorama RGBD in immersive VR experiences.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.