Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views (2312.04424v2)

Abstract: Synthesizing multi-view 3D from one single image is a significant but challenging task. Zero-1-to-3 methods have achieved great success by lifting a 2D latent diffusion model to the 3D scope. The target view image is generated with a single-view source image and the camera pose as condition information. However, due to the high sparsity of the single input image, Zero-1-to-3 tends to produce geometry and appearance inconsistency across views, especially for complex objects. To tackle this issue, we propose to supply more condition information for the generation model but in a self-prompt way. A cascade framework is constructed with two Zero-1-to-3 models, named Cascade-Zero123, which progressively extract 3D information from the source image. Specifically, several nearby views are first generated by the first model and then fed into the second-stage model along with the source image as generation conditions. With amplified self-prompted condition images, our Cascade-Zero123 generates more consistent novel-view images than Zero-1-to-3. Experiment results demonstrate remarkable promotion, especially for various complex and challenging scenes, involving insects, humans, transparent objects, and stacked multiple objects etc. More demos and code are available at https://cascadezero123.github.io.

• The paper introduces a two-stage cascade framework that progressively refines single-image 3D reconstruction through sequential Zero-1-to-3 models.
• The self-prompting mechanism generates intermediate nearby views that enhance geometric and visual consistency without substantial additional computational cost.
• Quantitative evaluations on Objaverse and RealFusion15 benchmarks show superior PSNR, SSIM, LPIPS, and CLIP-Score performance compared to prior models.

Insights into "Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views"

In the paper "Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views," the authors present a novel framework for 3D object reconstruction from a single 2D image. This work enhances existing Zero-1-to-3 methods by introducing a cascade strategy that applies a self-prompting mechanism to generate consistent multi-view images, overcoming limitations in geometric and visual consistency faced by prior models.

Contributions

The paper makes significant advancements in 3D reconstruction techniques by developing a two-stage cascade framework, Cascade-Zero123, comprised of two sequential Zero-1-to-3 models: Base-0123 and Refiner-0123. Each model progressively extracts 3D information, allowing the system to surmount the difficulties associated with rendering complex object structures from isolated viewpoints.

1. Cascade Framework:
   • The cascade arrangement refines the generation process by crafting multiple nearby views initially, which then inform the creation of the final target view. This architectural choice allows for the incremental building of 3D priors, essential for maintaining view consistency across varied angles.
2. Self-Prompting Mechanism:
   • The introduction of self-prompted nearby views serves as an intermediate stage that enhances geometric and visual coherence without substantial additional computational costs. This approach involves generating augmented viewpoint data through the first model and feeding this conditioned data into the subsequent model to improve consistency.
3. Evaluation and Results:
   • Quantitative assessments on benchmark datasets, such as the Objaverse and RealFusion15, display superior performance in terms of PSNR, SSIM, LPIPS, and CLIP-Score when compared to competing models such as Zero123-XL, SyncDreamer, and Magic123.

Implications

The theoretical and practical implications of this research are noteworthy:

• Theoretical Impact:
  • Cascade-Zero123 presents an innovative use of multi-stage diffusion models which could reshape approaches to learning and generating 3D content by exploiting temporal or iterative coherence principles inherent in cascaded architectures.
• Practical Applications:
  • The success of integrating multi-view conditions to achieve consistent 3D synthesis from single images holds substantial promise for applications in virtual reality, online gaming, and cinematic animations where such input-output transformations are required.
  • The framework's capability to maintain high fidelity across complex and occluded scenes extends its utility into areas such as automated modeling and digital twins in industrial design and manufacturing.

Future Directions

Future explorations could revolve around refining the cascade model's ability to handle highly occluded or asymmetric objects and extending these methods to integrate additional sensory modalities such as depth maps or semantic cues. Moreover, the incorporation of advanced attention mechanisms or alternative conditioning strategies could further advance the precision and scalability of single-image-to-3D translation frameworks.

In conclusion, this paper sets a solid foundation with Cascade-Zero123 by pioneering self-prompted, multi-view enhancements in diffusion-driven 3D synthesis, signifying a considerable leap in both theoretical understanding and practical application of 3D computer vision technologies.