GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion (2412.10209v2)

Abstract: We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to limited observations, which leaves unobserved regions under-constrained and can lead to artifacts in novel views. To address this problem, we introduce a multi-view head diffusion model, leveraging its priors to fill in missing regions and ensure view consistency in Gaussian splatting renderings. To enable precise viewpoint control, we use normal maps rendered from FLAME-based head reconstruction, which provides pixel-aligned inductive biases. We also condition the diffusion model on VAE features extracted from the input image to preserve facial identity and appearance details. For Gaussian avatar reconstruction, we distill multi-view diffusion priors by using iteratively denoised images as pseudo-ground truths, effectively mitigating over-saturation issues. To further improve photorealism, we apply latent upsampling priors to refine the denoised latent before decoding it into an image. We evaluate our method on the NeRSemble dataset, showing that GAF outperforms previous state-of-the-art methods in novel view synthesis. Furthermore, we demonstrate higher-fidelity avatar reconstructions from monocular videos captured on commodity devices.

• The paper introduces GAF, a method using multi-view diffusion and Gaussian Splatting to reconstruct high-fidelity 3D animatable avatars from single-camera videos.
• GAF significantly improves novel view synthesis compared to state-of-the-art methods, achieving a 5.34% higher SSIM on the NeRSemble dataset.
• This research democratizes high-fidelity avatar creation, enabling practical applications in VR, video conferencing, and entertainment using commodity devices.

Overview of GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion

The paper "GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion" introduces a sophisticated approach for constructing animatable 3D Gaussian avatars from monocular video inputs, utilizing a multi-view diffusion model. The methodology is especially relevant in contexts where high-fidelity head avatars are desired, typically in applications like virtual reality, video conferencing, and entertainment industries.

Methodological Insights

The primary challenge tackled by the authors is the inherent limitation in data captured from monocular setups—such as a single camera view from a smartphone—where unobserved regions lead to incomplete head reconstructions. The proposed solution involves a multi-view head diffusion model that leverages diffusion priors to predict and fill these unobserved gaps, ensuring consistency across novel views. The model conditions on pixel-aligned inductive biases through normal maps from FLAME-based head reconstructions, and incorporates VAE features for fine-grained identity and appearance preservation.

The GAF methodology diverges from traditional techniques by integrating Gaussian Splatting (GS) into the modeling process. This facilitates photo-realistic rendering with considerable computational efficiency compared to neuromorphic radiance field (NeRF)-based methods. The Gaussian splatting approach employs a density field of 3D Gaussians, a significant shift from typical mesh topology methods, to represent and render head avatars, aligning with dynamic avatar representations.

Numerical Results and Evaluation

The authors evaluate their approach using the NeRSemble dataset, where GAF demonstrates a substantial improvement in novel view synthesis, achieving a 5.34% higher SSIM compared to state-of-the-art methods. This outcome underscores GAF's capability to produce higher-fidelity renderings from monocular inputs, significantly advancing the photorealism and consistency of avatar reconstructions. The research effectively demonstrates the model's robustness across varying head rotations and configurations, solidifying its application potential in dynamic scenes.

Theoretical and Practical Implications

This research carries both theoretical and pragmatic implications. Theoretically, it advances the multi-view diffusion frameworks for 3D reconstruction by integrating robust priors and innovative conditioning strategies. Practically, the democratization of high-fidelity avatar creation from commodity devices suggests potential widespread accessibility of immersive virtual environments without sophisticated equipment.

The paper's focus on practical implementations potentially lays groundwork for further exploration in efficient, real-time avatar generation even from low-resource settings or low-resolution video inputs. Such advancements could pivotally influence virtual interfaces and content creation industries.

Speculation on Future Developments

Looking ahead, future research could optimize the computational aspects by incorporating advancements in real-time diffusion sampling techniques and enhanced data-driven learning paradigms. Another promising avenue for exploration is the extension of Gaussian splatting methods to encapsulate more complex expressions and environmental interactions, aiming for even greater levels of realism and expressiveness.

Moreover, addressing limitations such as avatar fidelity under varied lighting conditions remains crucial for comprehensive deployment across diverse applications. Ensuring ethical guidelines and security frameworks around avatar generation will be paramount to avoid potential misuse, such as creating unauthorized deepfakes.

In conclusion, the GAF approach delineates a significant step forward in 3D avatar reconstruction, presenting a technically robust and versatile framework for leveraging monocular video inputs to generate detailed, lifelike avatars. This research not only fortifies theoretical constructs in multi-view image diffusion but also propels forward practical applications, broadening the horizon of interactive and immersive virtual experiences.