High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies (2010.05562v2)

Abstract: We present a fully automatic system that can produce high-fidelity, photo-realistic 3D digital human heads with a consumer RGB-D selfie camera. The system only needs the user to take a short selfie RGB-D video while rotating his/her head, and can produce a high quality head reconstruction in less than 30 seconds. Our main contribution is a new facial geometry modeling and reflectance synthesis procedure that significantly improves the state-of-the-art. Specifically, given the input video a two-stage frame selection procedure is first employed to select a few high-quality frames for reconstruction. Then a differentiable renderer based 3D Morphable Model (3DMM) fitting algorithm is applied to recover facial geometries from multiview RGB-D data, which takes advantages of a powerful 3DMM basis constructed with extensive data generation and perturbation. Our 3DMM has much larger expressive capacities than conventional 3DMM, allowing us to recover more accurate facial geometry using merely linear basis. For reflectance synthesis, we present a hybrid approach that combines parametric fitting and CNNs to synthesize high-resolution albedo/normal maps with realistic hair/pore/wrinkle details. Results show that our system can produce faithful 3D digital human faces with extremely realistic details. The main code and the newly constructed 3DMM basis is publicly available.

• The paper presents a novel method that generates high-fidelity 3D digital human heads from RGB-D selfie videos with a processing time of under 30 seconds.
• The methodology combines an enhanced 3D morphable model fitting with CNN-based reflectance synthesis to capture intricate facial details.
• Comprehensive evaluations demonstrate superior accuracy in retaining unique facial features, offering significant advancements for VR, gaming, and personalized media applications.

High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies: A Professional Overview

This paper presents a significant advancement in the field of 3D digital human modeling by introducing a fully automated system capable of producing high-fidelity, photo-realistic 3D digital human heads from RGB-D selfie videos. Developed by researchers at Tencent AI Lab, this system requires no more than 30 seconds to generate a high-quality 3D head model, marking a substantial leap in user-friendly solutions for digital human creation.

Methodology

The central innovation revolves around a robust facial geometry modeling and reflectance synthesis process that uses a consumer-grade RGB-D camera, such as those commonly found in smartphones. The workflow begins with capturing an RGB-D video of the user as they slowly rotate their head. The system then employs a two-stage frame selection procedure to isolate high-quality frames for further processing.

The core method involves an enhanced 3D Morphable Model (3DMM) fitting algorithm that leverages a differentiable renderer to recover facial geometries. This 3DMM is designed with significantly improved expressive capabilities achieved through extensive data augmentation and perturbation techniques. The augmentation process allows the system to capture subtle individual nuances in facial shape using a linear basis.

Following the geometry capture, the system synthesizes reflectance maps, integrating parametric fitting with CNN-based enhancement to create high-resolution albedo and normal maps rich with intricate facial details like hair, pores, and wrinkles. This hybrid approach effectively marries traditional parametric models' precision with the detail generation capacity of neural networks.

The procedural steps are modular and optimized for efficiency. From frame selection to rendering, each component is designed to require minimal manual intervention, guaranteeing a streamlined user experience with computationally efficient processing.

Evaluation

The system's efficacy is demonstrated through comprehensive evaluations. Geometrically, the approach is validated against existing methods such as N-ICP, GANFIT, and AvatarMe, showing superior fidelity in capturing distinctive facial features, particularly around cheeks and mouth regions, where human perception sensitivity is highest. The texture synthesis method effectively augments these high-fidelity shapes with realistic skin details and color maps free from undesirable artifacts of lighting conditions.

Quantitatively, the system's ability to produce recognizable and accurate digital representations of users was confirmed through identity verification tests against a database of 36 subjects. The results underscored the higher precision of the synthesized models, further corroborated through both visual inspection and comparison metrics.

Practical and Theoretical Implications

Practically, this system democratizes high-fidelity digital human creation, providing accessibility with minimal hardware and reducing processing time significantly compared to prior methodologies. This has broad implications for industries such as gaming, virtual reality, and personalized media, where user-generated content demands both quality and speed.

Theoretically, the research presents a compelling case for data augmentation in expanding the expressive power of 3D models without increasing computational burdens traditionally associated with high-dimensional models. The innovative blend of differentiated rendering and neural network layers for reflectance maps opens new avenues in photorealistic rendering that is computationally feasible on consumer-grade devices.

Future Development

Despite its achievements, the paper notes certain limitations, such as handling cross-ethnicity generalization and reconstructing individual-specific elements like facial hair or extreme expressions. Addressing these issues involves further refinements in the 3DMM and potentially incorporating more advanced machine learning techniques to handle variability in datasets not represented during training.

In conclusion, the presented system constitutes a significant step forward in the domain of real-time, high-fidelity 3D human modeling from easily accessible data sources. By achieving a balance between detail richness and computational efficiency, this approach sets a foundation for continued innovations in digital avatar creation and immersive user experiences.