Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images (2409.20530v1)

Abstract: 3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predominantly built on EG3D, which specializes in synthesizing near-frontal views and is limiting in synthesizing comprehensive 3D scenes from diverse viewpoints. In contrast to existing approaches, we propose a novel framework built on PanoHead, which excels in synthesizing images from a 360-degree perspective. To achieve realistic 3D modeling of the input image, we introduce a dual encoder system tailored for high-fidelity reconstruction and realistic generation from different viewpoints. Accompanying this, we propose a stitching framework on the triplane domain to get the best predictions from both. To achieve seamless stitching, both encoders must output consistent results despite being specialized for different tasks. For this reason, we carefully train these encoders using specialized losses, including an adversarial loss based on our novel occlusion-aware triplane discriminator. Experiments reveal that our approach surpasses the existing encoder training methods qualitatively and quantitatively. Please visit the project page: https://berkegokmen1.github.io/dual-enc-3d-gan-inv.

• The paper presents a dual encoder system that separately processes visible and occluded regions to achieve accurate 3D head modeling.
• It employs an occlusion-aware triplane discriminator and specialized adversarial loss functions to seamlessly stitch inputs into a consistent 3D reconstruction.
• Quantitative results indicate robust performance across LPIPS, L2, ID, and FID metrics, highlighting its potential for VR, AR, and digital content creation.

Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images

The paper "Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images" by Bahri Batuhan Bilecen, Ahmet Berke Gokmen, and Aysegul Dundar presents an innovative framework for addressing the challenges of 3D GAN inversion, specifically within the context of PanoHead.

Overview and Motivation

3D GAN inversion involves embedding a single image into the latent space of a 3D GAN, facilitating the accurate reconstruction of 3D geometry. While existing methods have demonstrated efficacy in this arena, they frequently rely on EG3D, which focuses primarily on frontal views and thus falls short in rendering comprehensive 3D scenes from diverse viewpoints. This limitation necessitated the development of a more versatile approach, underpinning the novel framework introduced in the paper. By leveraging PanoHead, which is capable of synthesizing 360-degree views, this framework seeks to achieve high-fidelity 3D modeling using a dual encoder system tailored for both visible and occluded regions.

Methodology

The paper proposes a dual encoder system and an occlusion-aware triplane discriminator to address the limitations of prior methods:

1. Dual Encoder System: The dual encoder system is designed to strike a balance between high-fidelity image reconstruction and the generation of realistic representations of invisible regions. Encoder 1 focuses on high-fidelity reconstruction from the given view, while Encoder 2 is optimized using an adversarial loss to produce realistic predictions for occluded regions.
2. Occlusion-Aware Triplane Discriminator: The novel discriminator operates in the triplane domain, trained exclusively on features from occluded pixels. This discriminator ensures that both encoders produce consistent and complementary outputs, enabling seamless stitching of the final 3D model. This approach mitigates the distribution mismatch between encoded and synthesized triplanes, enhancing the fidelity and realism of the 3D reconstructions.

The proposed method employs a stitching framework that synthesizes the most accurate predictions from both encoders in the triplane domain, ensuring consistency and avoiding artifacts. Specialized loss functions, including an adversarial loss, guide the training of the encoders to achieve the desired output quality.

Evaluation and Results

The experiments conducted illustrate the superiority of the proposed framework over existing methods, both quantitatively and qualitatively. Key findings include:

• Quantitative Metrics: The framework demonstrates competitive performance in reconstruction fidelity as evidenced by LPIPS, $\mathcal{L}_2$, and identity (ID) metrics. It also significantly improves upon state-of-the-art in terms of Fréchet Inception Distance (FID) for novel views, underscoring the capability to generalize well across diverse camera angles.
• Qualitative Analysis: The proposed method produces visually compelling results that are consistent and realistic from any viewpoint, outperforming contemporary methods like PTI, pSp, e4e, TriplaneNetv2, and GOAE in reconstructing the invisible parts of the head and ensuring 360-degree fidelity.
• Efficiency: While optimization-based methods like PTI are memory-intensive and time-consuming, the dual encoder system offers a more practical, faster alternative without compromising on the quality of the reconstructions.

Implications and Future Work

The advancements presented by the dual encoder GAN inversion framework have significant implications for fields requiring high-fidelity 3D modeling from single images, such as virtual reality (VR), augmented reality (AR), and digital content creation. Practically, it facilitates applications such as animating static portraits, creating digital avatars, and enhancing gaming environments.

Theoretically, this work addresses key limitations of existing 3D GAN inversion methods by effectively balancing fidelity and realism using dual encoders and specialized loss objectives. Future work could explore further enhancements in feature consistency, improvements in handling occlusions, and extending the framework to accommodate more diverse datasets, including those with high-frequency details or various accessories.

Conclusion

The "Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images" introduces a robust and efficient framework that leverages a dual encoder system, specialized loss functions, and an occlusion-aware discriminator to achieve high-fidelity, realistic 3D reconstructions. This paper makes compelling advancements in the field of 3D generative models and opens avenues for more versatile applications in digital content creation and beyond.