Synthetic Prior for Few-Shot Drivable Head Avatar Inversion (2501.06903v3)

Abstract: We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle three major challenges. First, training a controllable 3D generative network requires a large number of diverse sequences, for which pairs of images and high-quality tracked meshes are not always available. Second, the use of real data is strictly regulated (e.g., under the General Data Protection Regulation, which mandates frequent deletion of models and data to accommodate a situation when a participant's consent is withdrawn). Synthetic data, free from these constraints, is an appealing alternative. Third, state-of-the-art monocular avatar models struggle to generalize to new views and expressions, lacking a strong prior and often overfitting to a specific viewpoint distribution. Inspired by machine learning models trained solely on synthetic data, we propose a method that learns a prior model from a large dataset of synthetic heads with diverse identities, expressions, and viewpoints. With few input images, SynShot fine-tunes the pretrained synthetic prior to bridge the domain gap, modeling a photorealistic head avatar that generalizes to novel expressions and viewpoints. We model the head avatar using 3D Gaussian splatting and a convolutional encoder-decoder that outputs Gaussian parameters in UV texture space. To account for the different modeling complexities over parts of the head (e.g., skin vs hair), we embed the prior with explicit control for upsampling the number of per-part primitives. Compared to SOTA monocular and GAN-based methods, SynShot significantly improves novel view and expression synthesis.

• The paper "Synthetic Prior for Few-Shot Drivable Head Avatar Inversion" introduces SynShot, a novel method for creating high-quality personalized 3D head avatars from a minimal number of input images.
• SynShot leverages a generative network trained on a large synthetic dataset (approx. 14M images from 2k identities) to establish a strong prior, which is then fine-tuned using only a few real-world images.
• Results show SynShot significantly outperforms state-of-the-art monocular methods in generating novel expressions and views, achieving superior photorealistic quality with as few as three input images.

The paper "Synthetic Prior for Few-Shot Drivable Head Avatar Inversion" introduces a method named SynShot for generating high-quality personalized 3D Gaussian head avatars from a minimal number of input images. This approach is motivated by the limitations faced when relying solely on monocular imaging techniques and the high resource demand of capturing diverse and high-quality datasets required for training state-of-the-art (SOTA) head avatar models.

Methodology

SynShot leverages a synthetic data-driven approach to construct a strong prior for 3D head avatars, overcoming the typical constraints of requiring large multi-view datasets. The method hinges on the following components:

1. Generative Gaussian Head Avatar: At the core of SynShot is a 3D generative network trained on a vast synthetic dataset that encompasses a wide variety of head shapes, expressions, and viewpoints. This network utilizes a convolutional encoder-decoder architecture, and importantly, employs a unique Gaussian 3D splatting technique for rendering.
2. Synthetic Dataset: The dataset, which drives the training of SynShot, comprises approximately 14 million synthetic images derived from around 2,000 unique identities with diverse hair, beards, and detailed facial features. This allows SynShot to model a wide expression and viewpoint space without the need for expensive and complex real-world data capture.
3. Few-Shot Fine-Tuning: SynShot performs a pivotal tuning strategy, which involves projecting a few real input images onto the learned synthetic prior manifold. It proceeds to fine-tune the appearance networks of the avatar model to better fit the input images by optimizing identity and expression latent spaces.
4. Reconstruction with UV Texture Space: The network models head appearances using a 3D Gaussian splatting representation where Gaussian parameters are predicted directly into the UV texture space. This enables part-based densification, crucially enhancing the adaptability and quality of rendered hair and facial regions without predefined mesh templates.

Results

The authors demonstrate that SynShot significantly outperforms SOTA monocular methods when it comes to novel view and expression synthesis from very few images. In comprehensive evaluation scenarios, including self and cross-reenactment and few-shot inversion, SynShot outperforms existing personalized avatar methods, which typically require thousands of image frames for training.

• Novel Expressions and Views: The method shows a robust ability to generalize new facial expressions and perspectives that were not captured in the input images, highlighting the strength of the synthetic prior.
• Inversion Performance: Systematic comparisons reveal that using only three input images, SynShot can achieve superior photorealistic reconstructive quality compared to recent NeRF-based and monocular avatars.
• Single Image Reconstruction: Even in the extreme scenario of single-image inversion, SynShot maintains superior geometric consistency and detail fidelity, especially against methods like MoFaNeRF and HeadNeRF.

Challenges and Limitations

The paper acknowledges several challenges, such as the existing domain gap between synthetic and real data particularly concerning generalization to lighting and appearance variations not represented in the synthetic data. Additionally, certain facial accessories or uncommon hairstyles not represented in the synthetic dataset can lead to reconstruction errors.

Conclusion

In summary, SynShot presents a significant advancement in the generation of personalized 3D avatars using synthetic data to define a robust prior, requiring significantly fewer input images than current methods. This approach could have wide-ranging applications in VR, mixed reality, and digital telepresence, where high-quality, versatile digital avatars are increasingly essential. Future efforts might focus on expanding the synthetic dataset’s diversity to further close the domain gap with real-world conditions.