Neural Head Avatars from Monocular RGB Videos (2112.01554v2)

Abstract: We present Neural Head Avatars, a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar that can be used for teleconferencing in AR/VR or other applications in the movie or games industry that rely on a digital human. Our representation can be learned from a monocular RGB portrait video that features a range of different expressions and views. Specifically, we propose a hybrid representation consisting of a morphable model for the coarse shape and expressions of the face, and two feed-forward networks, predicting vertex offsets of the underlying mesh as well as a view- and expression-dependent texture. We demonstrate that this representation is able to accurately extrapolate to unseen poses and view points, and generates natural expressions while providing sharp texture details. Compared to previous works on head avatars, our method provides a disentangled shape and appearance model of the complete human head (including hair) that is compatible with the standard graphics pipeline. Moreover, it quantitatively and qualitatively outperforms current state of the art in terms of reconstruction quality and novel-view synthesis.

• The paper introduces a hybrid method that fuses a morphable model with neural networks to capture detailed facial geometry and expressions.
• It achieves superior 3D consistency and identity preservation through explicit modeling of geometry and view-dependent textures.
• The approach optimizes high-quality 4D avatars from short monocular videos, paving the way for advanced VR, AR, and teleconferencing applications.

Neural Head Avatars from Monocular RGB Videos

The paper "Neural Head Avatars from Monocular RGB Videos" introduces a novel approach in the domain of reconstructing and animating human head avatars using monocular video inputs. While existing methodologies often rely on either implicit representations or generalized image-based models, this research adopts a hybrid approach combining a morphable model and neural networks to create a detailed and animatable 4D avatar, which includes geometric and photorealistic texture modeling.

Technical Contributions

This work offers several technical advancements:

1. Hybrid Representation: The method utilizes a morphable model for capturing coarse facial shapes and expressions. Additionally, two feed-forward neural networks are employed for refining vertex offsets and generating view and expression-dependent textures. This hybrid approach ensures compatibility with traditional graphics pipelines while maintaining high fidelity in synthesis results.
2. Explicit Shape and Appearance Model: By explicitly modeling the geometry, including hair, the proposed method enhances 3D consistency and identity preservation, outperforming previous approaches in reconstruction quality and novel-view synthesis.
3. Optimization from Monocular Input: The methodology demonstrates that high-quality 4D head avatars can be optimized from short monocular RGB video sequences, circumventing the limitations of multi-view setups.

Evaluation and Comparison

The paper presents a comprehensive evaluation of the model's performance on both synthetic and real datasets. Quantitative analyses, including angular error in normals and Hausdorff distance in mesh alignment, underscore the superior geometry reconstruction capability of the model compared to existing benchmarks like the FLAME model. Furthermore, the model's capacity for novel pose and view synthesis is validated through diverse qualitative comparisons against state-of-the-art methods such as NerFACE and Deep Video Portraits.

Implications and Future Directions

The potential applications of Neural Head Avatars are vast, spanning virtual reality (VR), augmented reality (AR), teleconferencing, gaming, and digital cinema. The precision in expression and pose modeling may pave the way for more nuanced and realistic avatars, augmenting interactive experiences and enhancing remote collaborations. A promising avenue for future work involves addressing dynamic elements like hair physics and generalizing the approach to handle a broader range of identities without requiring subject-specific optimization. Additionally, integrating features for synthesizing effects like lighting and intricate textures could further advance realism.

Ethical Considerations

The paper briefly acknowledges the ethical implications of such technology, stressing the importance of safeguards against misuse, such as misinformation. Future work in media authenticity verification, including cryptographic certification and passive forgery detection, will be vital in ensuring responsible deployment of avatar synthesis technologies.

In conclusion, the approach posited in this paper is a significant contribution to the field, detailing a method that blends conventional graphics pipeline compatibility with the robustness and flexibility of neural modeling, presenting a stride forward in the field of human head reconstruction from limited data inputs. Although further refinements are necessary to fully encapsulate the complexities of human features, the foundational aspects established herein set a benchmark for future developments.