Envision3D: One Image to 3D with Anchor Views Interpolation (2403.08902v1)

Abstract: We present Envision3D, a novel method for efficiently generating high-quality 3D content from a single image. Recent methods that extract 3D content from multi-view images generated by diffusion models show great potential. However, it is still challenging for diffusion models to generate dense multi-view consistent images, which is crucial for the quality of 3D content extraction. To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation. In the first stage, we train the image diffusion model to generate global consistent anchor views conditioning on image-normal pairs. Subsequently, leveraging our video diffusion model fine-tuned on consecutive multi-view images, we conduct interpolation on the previous anchor views to generate extra dense views. This framework yields dense, multi-view consistent images, providing comprehensive 3D information. To further enhance the overall generation quality, we introduce a coarse-to-fine sampling strategy for the reconstruction algorithm to robustly extract textured meshes from the generated dense images. Extensive experiments demonstrate that our method is capable of generating high-quality 3D content in terms of texture and geometry, surpassing previous image-to-3D baseline methods.

• The paper presents a cascade diffusion framework that splits 3D generation into anchor views production and their interpolation.
• It introduces an Instruction Representation Injection module with multi-view and cross-domain attention to ensure semantic and geometric consistency.
• Empirical results show improved PSNR, SSIM, and Chamfer Distance metrics, demonstrating robust 3D reconstructions that outperform baseline methods.

Analysis of "Envision3D: One Image to 3D with Anchor Views Interpolation"

The paper "Envision3D: One Image to 3D with Anchor Views Interpolation" presents an innovative approach to generating high-quality 3D content from a single image, leveraging the capabilities of diffusion models. This research specifically addresses the challenges faced in extracting 3D information from single images which include multi-view inconsistency and high computational demands typically associated with diffusion-based methods.

Problem Context and Methodological Advances

The authors propose a cascade diffusion framework that significantly enhances the generation of dense, multi-view consistent images, ultimately leading to superior 3D content extraction. The methodology is divided into two main stages: anchor views generation and anchor views interpolation. This bifurcation of tasks allows the approach to manage the complexity inherent in generating densely populated view sets.

1. Anchor Views Generation: This stage involves training a multi-view diffusion model enhanced with multi-view attention and cross-domain attention mechanisms. To accelerate convergence and improve alignment, the introduction of an Instruction Representation Injection (IRI) module is crucial, as it injects pre-aligned image-normal pairs into diffusion processes. This module aligns the semantic consistency of the produced views by using geometry-aware conditioning derived from predicted normal maps.
2. Anchor Views Interpolation: This stage utilizes a video diffusion model—adapted for multiple views generation—to interpolate additional views between the anchor points. Fine-tuning this model introduces efficiency in generating additional views while maintaining consistency, leveraging the inherent 3D understanding in video data processing. The model efficiently handles the interpolation through spatial-temporal architectures inherent to video diffusion models.
3. Robust 3D Reconstruction: Following the generation of dense consistent images, the authors apply a coarse-to-fine sampling strategy within the mesh extraction phase. This method begins by establishing a basic global texture and geometry using anchor views, then refines these with details from interpolation views, thereby enhancing the quality and robustness of the extracted 3D content.

Empirical Evaluation and Implications

The authors extensively evaluate Envision3D on datasets such as Google Scanned Objects and additional collected images, demonstrating its effectiveness over baseline methods like Zero123, SyncDreamer, and Wonder3D in generating 3D content with improved texture clarity and geometric precision. The introduction of 32-view consistent images marks a significant departure from previous approaches regarding the quality of rendered 3D models.

The paper's numerical results, presenting metrics such as PSNR, SSIM, and LPIPS for synthesized and re-rendered views, reflect significant improvements, for instance, achieving a PSNR of 20.00 on re-rendered views which outperforms existing methods. The low Chamfer Distance and high Volume IoU metrics further underscore the paper's claims regarding geometric fidelity.

Theoretical and Practical Implications

This research contributes notably to the domain of 3D modeling from 2D inputs, offering a scalable solution to previously untenable computational challenges in single-image 3D rendition. The tailored architecture of the diffusion models and their adaptive training strategies potentially pave the way for applications in VR, gaming, and even automatized content generation scenarios in robotics. Future exploration could focus on further integrating knowledge from extensive 3D datasets and improving training efficiencies, potentially refining the anchor-interpolation strategy for different classes of objects or environments.

Conclusion

Envision3D embodies a substantive advancement in image-to-3D conversion technologies. By segregating the complex task of dense view generation into tractable processes, it delivers a robust model that overcomes limitations of prior diffusion-based methods. This work not only enhances the theoretical understanding of 3D content generation but also extends practical capabilities in industries reliant on efficient 3D modeling technologies.