Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior (2303.14184v2)

Abstract: In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization pipeline: the first stage optimizes a neural radiance field by incorporating constraints from the reference image at the frontal view and diffusion prior at novel views; the second stage transforms the coarse model into textured point clouds and further elevates the realism with diffusion prior while leveraging the high-quality textures from the reference image. Extensive experiments demonstrate that our method outperforms prior works by a large margin, resulting in faithful reconstructions and impressive visual quality. Our method presents the first attempt to achieve high-quality 3D creation from a single image for general objects and enables various applications such as text-to-3D creation and texture editing.

• The paper introduces a two-stage method that uses 2D diffusion priors to guide neural radiance field optimization and generate refined textured point clouds.
• It demonstrates superior reconstruction quality by significantly improving LPIPS, contextual distance, and CLIP scores compared to previous methods.
• The approach opens pathways for versatile applications in VR, digital art, and interactive design, setting a foundation for future cross-modal 3D modeling research.

Overview of "Make-It-3D: High-Fidelity 3D Creation from a Single Image with Diffusion Prior"

This paper presents "Make-It-3D," an innovative method for synthesizing high-fidelity 3D content from a single image using diffusion prior. The authors address a core challenge in computer vision and graphics: inferring 3D geometry and unseen textures from just a single viewpoint. This task inherently demands advanced techniques to generate plausible 3D representations.

Methodology

The proposed method leverages a well-trained 2D diffusion model to guide the 3D creation process, using it as a form of 3D-aware supervision. Make-It-3D utilizes a two-stage optimization pipeline:

1. Neural Radiance Field Optimization: Initially, the approach optimizes a neural radiance field (NeRF), integrating constraints from the reference image's frontal view alongside diffusion prior knowledge for novel views. The method incorporates a score distillation sampling (SDS) technique to ensure the NeRF captures plausible 3D geometry aligned with the reference image.
2. Textured Point Clouds: In the subsequent phase, the coarse model derived from the first stage is transformed into textured point clouds. This stage aims to enhance realism by refining textures with diffusion prior, while textures within the visible regions are directly inherited from the reference image. By projecting and enhancing textures, the method significantly improves the realism of the resulting 3D model.

Results and Validation

Extensive experiments demonstrate that Make-It-3D outperforms prior methodologies by a considerable margin, achieving accuracy and visual detail in the 3D reconstructions. Among the metrics used for evaluation were LPIPS for image similarity, contextual distance, and CLIP score for semantic alignment, all showing notable improvements compared to baseline models.

Applications and Implications

Make-It-3D's ability to generate high-fidelity 3D models from a single image presents implications across various fields. For example, it opens up possibilities in highly realistic 3D content creation with applications transcending domains such as virtual reality, digital art, and interactive design. Furthermore, the technique isn't confined to specific object categories, enhancing its versatility for creative endeavors, from text-to-3D conversion to texture editing.

From a theoretical perspective, the paper highlights the latent potential of 2D diffusion models to encapsulate 3D knowledge, suggesting future pathways for development in cross-modal learning and 3D model generation without explicit multi-view datasets.

Speculation on Future Developments

As for future developments, the integration of more sophisticated priors, such as dynamic 3D priors or hybrid models combining multiple data modalities, could further enhance the fidelity and diversity of generated 3D content. Researchers might also explore optimizing the computational efficiency of such methods to extend their applicability in real-time systems. Additionally, the expansion into temporal data for tasks such as 4D scene reconstruction could be a promising direction, leveraging the strengths of diffusion models in time-series generation.

In conclusion, "Make-It-3D" signifies a substantive advance in leveraging diffusion models for 3D content creation, suggesting wide-ranging applications and offering a strong foundation for future research endeavors in 3D modeling and beyond.