ViewFusion: Towards Multi-View Consistency via Interpolated Denoising (2402.18842v1)

Abstract: Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this, we introduce ViewFusion, a novel, training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation, ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising, our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views.

• The paper presents a training-free algorithm that integrates auto-regressive techniques to enforce multi-view consistency in novel-view synthesis.
• It employs interpolated denoising to fuse prior view information, leading to superior image quality as measured by SSIM, PSNR, and LPIPS.
• The approach requires no fine-tuning, offering practical benefits for applications in 3D reconstruction, computer graphics, and augmented reality.

An Expert Overview of "ViewFusion: Towards Multi-View Consistency via Interpolated Denoising"

The paper "ViewFusion: Towards Multi-View Consistency via Interpolated Denoising" presents a noteworthy algorithm addressing the challenges associated with multi-view consistency in the domain of novel-view synthesis using diffusion models. It acknowledges the limitations of existing methods that independently generate images, resulting in significant challenges regarding maintaining consistent viewpoints. ViewFusion offers a sophisticated solution to this problem by integrating an auto-regressive approach into the diffusion processes to ensure robust consistency across generated views.

Key Contributions and Methodology

The primary contribution of the paper is the development and introduction of ViewFusion, a training-free algorithm that can be seamlessly integrated with pre-existing diffusion models. The architecture cleverly circumvents the need for retraining or fine-tuning while facilitating the transition from single-view conditioned models to multi-view conditioned frameworks. This adaptability is achieved by employing auto-regressive techniques that leverage previously generated views as context for generating subsequent views.

A distinctive aspect of ViewFusion lies in its interpolated denoising mechanism. This process involves using a diffusion framework to fuse known view information, capitalizing on interpolated denoising to extend single-view conditioned models. It also ensures consistency by sequentially conditioning each newly generated view on a set of previously synthesized views.

The paper highlights several advantages of ViewFusion:

1. Multi-Input Capability: ViewFusion can leverage all available views for guidance, thus enhancing image generation quality.
2. No Additional Fine-Tuning Required: It transforms pre-trained single-view conditioned diffusion models to handle multi-view scenarios effortlessly.
3. Flexibility in Weight Assignment: It allows adaptive weight settings for conditioning images based on relative view distance to the target view, optimizing the synthesis process.

Empirical Validation

The experimental results demonstrate the effectiveness of ViewFusion across multiple datasets. The paper utilizes the ABO and GSO datasets, providing empirical comparisons with baseline methods such as Zero123 and SyncDreamer. The findings emphasize superior performance, particularly in terms of multi-view consistency, assessed through metrics like SSIM, PSNR, LPIPS, and 3D reconstruction fidelity.

The paper also evaluates the utility of interpolated denoising using extensive empirical analysis, affirming ViewFusion’s ability to generate more consistent and detail-rich views. Notably, the algorithm exhibits significant potential in improving 3D reconstruction from novel-view images using existing generative models without additional training requirements.

Implications and Future Directions

From a theoretical perspective, ViewFusion represents a significant stride in advanced image modeling using diffusion processes, contributing to the evolving understanding of auto-regressive modeling techniques in multi-view applications. Practically, its capacity to improve consistency in image and 3D model reconstruction holds promise for applications in fields like computer graphics, augmented reality, and autonomous vehicle systems.

Future research directions could explore broader applications and adaptations of ViewFusion, potentially incorporating real-world complexities such as variable lighting conditions or occlusions across imagery. Further exploration could also address potential scenarios where even more complex scene dynamics are involved, such as dynamic object interactions in video sequences.

In summary, the paper presents a substantial theoretical and practical advancement in achieving multi-view consistency through an innovative integration of interpolated denoising processes with existing diffusion models. The approach not only enhances the quality of generated views but also offers a framework that could inspire subsequent research and application breakthroughs in fields involving complex image synthesis and 3D modeling.