SyncDreamer: Generating Multiview-consistent Images from a Single-view Image (2309.03453v2)

Abstract: In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.

SyncDreamer: Enhancing Multiview Image Generation from Single-view Inputs

The paper introduces SyncDreamer, a sophisticated diffusion model designed to generate multiview-consistent images from a single-view input. Addressing a notable challenge in 3D reconstruction and novel view synthesis, SyncDreamer provides a coherent continuation of prior developments in diffusion-based image generation, especially catering to consistency in both geometry and colors.

Key highlights and technical contributions of the paper include:

• Synchronized Multiview Diffusion Model: The core innovation in SyncDreamer lies in modeling the joint probability distribution of multiview images. This is realized through synchronized noise predictors working collectively to produce consistent images across various views. This strategy differs from independently generating views, which often leads to inconsistencies in appearance or geometry.
• 3D-aware Feature Attention: A novel architectural addition, the 3D-aware attention mechanism ensures multiview consistency by correlating features across different views. The spatial volume constructed from noise states allows the network to maintain both local and global coherence across generated views. This attention method captures essential relationships between views, critical for preserving object consistency and global geometry understanding.
• Generalization and Training: SyncDreamer leverages pretrained weights from Zero123, which are finetuned versions of the stable diffusion models, ensuring a strong starting point in terms of generalization. The model is further trained on Objaverse, enabling it to adapt to various domains, including photorealistic images and artistic sketches, with minimal manual intervention on training strategies.
• Robustness in Novel-view Synthesis: Besides generating multiview-consistent images, SyncDreamer integrates with existing 3D reconstruction tools like NeuS without specialized losses, streamlining the process from image generation to 3D model creation. In benchmarks, it achieves superior quantitative results, as evidenced by metrics such as PSNR, SSIM, and LPIPS compared to existing methodologies like Zero123 and RealFusion.

Upon evaluation, SyncDreamer not only demonstrates improved qualitative and quantitative performance in view consistency but also shows versatility across diverse style inputs, including hand drawings and cartoons, showcasing its potential applicability in varied computer vision tasks.

Implications and Speculations for AI Development:

Practically, SyncDreamer presents a significant step toward automating and enhancing the quality of 3D models from minimal input data. The model aids applications ranging from virtual reality content creation to architectural visualization, demanding seamless 3D reconstructions. Theoretically, it bridges gaps in understanding and modeling geometrical relationships in generative tasks, propelling the development of more advanced diffusion models capable of intuitive 3D structure generation.

Future research directions may include expanding the multiview generation capabilities to handle denser viewpoint grids or integrating orthographic projection support for various design applications. Additionally, enhancing the dataset quality, perhaps leveraging larger, better-curated datasets, could further improve the fidelity and applicability of the generated 3D structures.

In conclusion, SyncDreamer exemplifies a leap in diffusion models for 3D reconstruction, laying vital groundwork for subsequent advances in AI-driven visual processing tasks. Through methodical paper and pragmatic design choices, it presents researchers and industry practitioners with new tools to achieve more reliable and versatile 3D content generation.