X-Avatar: Expressive Human Avatars (2303.04805v2)

Abstract: We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. To achieve this, we propose a part-aware learned forward skinning module that can be driven by the parameter space of SMPL-X, allowing for expressive animation of X-Avatars. To efficiently learn the neural shape and deformation fields, we propose novel part-aware sampling and initialization strategies. This leads to higher fidelity results, especially for smaller body parts while maintaining efficient training despite increased number of articulated bones. To capture the appearance of the avatar with high-frequency details, we extend the geometry and deformation fields with a texture network that is conditioned on pose, facial expression, geometry and the normals of the deformed surface. We show experimentally that our method outperforms strong baselines in both data domains both quantitatively and qualitatively on the animation task. To facilitate future research on expressive avatars we contribute a new dataset, called X-Humans, containing 233 sequences of high-quality textured scans from 20 participants, totalling 35,500 data frames.

• The paper introduces a novel animatable implicit human model that uses part-aware learned forward skinning for accurate skeletal deformations.
• The methodology employs occupancy and deformation networks to capture high-resolution geometry and realistic motion from both 3D scans and RGB-D inputs.
• Experimental results demonstrate superior hand and face articulation with improvements in metrics such as Chamfer distance and IoU over current baselines.

X-Avatar: Expressive Human Avatars

The paper "X-Avatar: Expressive Human Avatars" presents a novel approach to the creation and animation of digital human avatars, specifically focusing on capturing the full richness of human expression, including body and hand poses, facial expressions, and clothing details. The authors propose an implicit modeling framework that overcomes limitations of current explicit and implicit methods, offering comprehensive and high-fidelity avatar representations.

Technical Contributions

X-Avatar introduces an animatable implicit human model that utilizes part-aware learned forward skinning to operate within the SMPL-X parameter space. This approach supports expressive animations derived from either full 3D scans or RGB-D input data, thus enhancing its practicality for applications in telepresence and augmented/virtual reality environments. Key components of the X-Avatar system include:

1. Geometry Modeling: Utilizes an occupancy network conditioned on body pose and facial expressions to predict high-resolution geometry of human avatars in canonical space.
2. Deformation Network: Employs a learned forward linear blend skinning (LBS) method to compute correspondences between deformed and canonical spaces, facilitating robust and realistic skeletal deformation.
3. Texture Network: Extends upon the geometry and deformation fields, modeling appearance attributes like high-frequency details while being conditioned on pose and localized surface normals.

The architecture innovatively incorporates part-specific sampling and initialization strategies that optimize the inclusion of smaller articulated regions such as hands and faces within the lattice of the human skeletal structure, a crucial advancement that addresses scale-related deficiencies in earlier methods.

Experimental Validation and Results

X-Avatar's performance is benchmarked against existing methods such as SCANimate and SNARF. The system is evaluated using the GRAB dataset and a newly introduced dataset, X-Humans, which contains extensive sequences of high-quality textured scans of diverse participants. The results illustrate significant improvements in animation quality:

• Quantitative Metrics: X-Avatar shows enhanced geometry accuracy, as measured by metrics like Chamfer distance, IoU, and normal consistency. Importantly, it demonstrates superior performance in modeling hand articulation and facial expressions when compared to current baselines.
• Qualitative Assessment: The model generates more plausible animations, displaying higher fidelity to intricate details like clothing textures and facial nuances.

Implications and Future Prospects

The development of X-Avatar represents a step toward more detailed and expressive digital humans, which are essential for the next generation of immersive media applications. The research introduces methodologies that pave the way for more effective integration of implicit neural models in avatar creation and pose a viable alternative to traditional mesh-based approaches. The integration of part-aware strategies within the broader framework notably enhances system efficiency and accuracy.

Future research could explore the potential of such models to generalize across multiple identities and adapt to dynamic environments with minimal training, possibly through the incorporation of more advanced learning algorithms and architectures. Further exploration into reducing the computational overhead and improving real-time applicability will likely enhance the model's adoption in the industry.

Overall, X-Avatar signifies valuable progress in the domain of digital human modeling, setting a reference point for future advancements in realistic and expressive human avatars.