DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans (2404.00485v1)

Abstract: We present DiffHuman, a probabilistic method for photorealistic 3D human reconstruction from a single RGB image. Despite the ill-posed nature of this problem, most methods are deterministic and output a single solution, often resulting in a lack of geometric detail and blurriness in unseen or uncertain regions. In contrast, DiffHuman predicts a probability distribution over 3D reconstructions conditioned on an input 2D image, which allows us to sample multiple detailed 3D avatars that are consistent with the image. DiffHuman is implemented as a conditional diffusion model that denoises pixel-aligned 2D observations of an underlying 3D shape representation. During inference, we may sample 3D avatars by iteratively denoising 2D renders of the predicted 3D representation. Furthermore, we introduce a generator neural network that approximates rendering with considerably reduced runtime (55x speed up), resulting in a novel dual-branch diffusion framework. Our experiments show that DiffHuman can produce diverse and detailed reconstructions for the parts of the person that are unseen or uncertain in the input image, while remaining competitive with the state-of-the-art when reconstructing visible surfaces.

• The paper introduces a probabilistic diffusion model that generates diverse, high-fidelity 3D human reconstructions from a single RGB image.
• It employs a dual-branch diffusion framework that accelerates the process by 55x while improving geometric detail and color accuracy.
• Empirical results demonstrate that DiffHuman outperforms state-of-the-art methods, offering realistic avatars for applications like VR, gaming, and digital content production.

Unveiling DiffHuman: A Probabilistic Approach to Photorealistic 3D Human Reconstruction from a Single Image

Introduction

The task of photorealistic 3D human reconstruction from a single RGB image presents an ill-posed yet vital challenge for various applications, including virtual and mixed reality, gaming, and digital content production. Traditional deterministic methods approach this problem by generating a single solution, which can lead to blurred details and inaccurate reconstructions, particularly in regions that are not visible in the input image. This paper introduces a novel probabilistic method, termed DiffHuman, which leverages conditional denoising diffusion models to generate multiple plausible and detailed 3D human reconstructions from a single input image, significantly enhancing the quality and diversity of the generated models.

Key Contributions

DiffHuman's methodological advancements and contributions are multi-faceted and can be encapsulated in the following points:

• Introduction of a probabilistic diffusion model tailored for the photorealistic 3D reconstruction of humans, capable of generating a distribution of plausible reconstructions conditioned on a single input image.
• A novel dual-branch diffusion framework that integrates an image generation network, significantly reducing the runtime by a factor of 55 compared to traditional rendering-based approaches.
• Empirical demonstrations of the model's ability to enhance the geometric detail and color accuracy in unseen or uncertain regions of the person in the input image, surpassing the existing state-of-the-art methods in quality.

Method Overview

At the heart of DiffHuman is a conditional diffusion model that intricately represents the intricate process from a noisy observation to a clean (denoised) 3D representation. This is achieved through a series of forward and reverse diffusion steps that iteratively refine the prediction. Unlike existing models that predominantly rely on one-way deterministic mappings, DiffHuman treats the reconstruction problem as a distribution prediction task. This allows for the sampling of diverse 3D avatars that exhibit high fidelity to the original input while also covering variations that are plausible but not directly observable in the input.

Implications and Future Directions

The probabilistic nature of DiffHuman introduces a paradigm shift in 3D human reconstruction. The ability to produce multiple detailed avatars from a single 2D image opens up new avenues for personalized content creation and interactive applications where user engagement benefits from variability and realism. Moreover, the efficiency gains from the dual-branch diffusion framework suggest that such probabilistic models can be feasibly integrated into real-time applications, breaking the computational barriers associated with high-quality 3D reconstruction.

Looking ahead, further exploration into refining the diffusion process and integration with other modalities (e.g., text descriptors or partial depth information) could enhance the model's applicability and accuracy. Additionally, investigating unsupervised or semi-supervised training regimes might alleviate the reliance on large-scale annotated datasets and expand the scope of reconstructable human poses and appearances.

Conclusion

DiffHuman represents a significant step forward in the photorealistic 3D reconstruction of humans from monocular images. By embracing a probabilistic modeling approach, it not only addresses the intrinsic ambiguities present in this task but also enriches the toolbox available for digital human modeling with a flexible and efficient solution. Moving forward, the continued development in this direction promises to further bridge the gap between the virtual and real, enriching digital experiences with more lifelike human avatars.