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Researchers From ETH Zurich and Microsoft Propose X-Avatar: An Animatable Implicit Human Avatar Model Capable of Capturing Human Body Pose and Facial ...

X-Avatar suggests a human model characterized by articulated neural implicit surfaces that accommodate the diverse topology of clothed individuals and achieve improved geometric resolution and increased fidelity of overall look. [Paper](https://arxiv.org/pdf/2303.04805.pdf), [Project,](https://skype-line.github.io/projects/X-Avatar/) and [Github](https://github.com/Skype-line/X-Avatar). The researchers begin with the parameter space of SMPL-X, an SMPL extension that captures the shape, look, and deformations of full-body people, paying special attention to hand positions and facial emotions to generate expressive and controllable human avatars. Researchers augment the geometry and deformation fields with a texture network conditioned by position, facial expression, geometry, and the deformed surface’s normals to capture the avatar’s look with high-frequency details. At the same time, a unique part-aware initialization method considerably enhances the result’s realism by raising the sample rate for smaller body parts. The group behind this initiative keeps their fingers crossed that their method may inspire more studies to give AIs more personality. Model X-Avatar may take in either a 3D posed scan or an RGB-D picture for processing. The technique can learn from either complete 3D scans or RGB-D data, producing comprehensive models of bodies, hands, facial emotions, and looks. Researchers present unique part-aware sampling and initialization algorithms to train the neural shape and deformation fields effectively. Researchers demonstrate empirically that the approach achieves superior quantitative and qualitative results on the animation task compared to strong baselines in both data areas. Pose, look, facial expression, hand gestures, etc.—collectively called “body language”—has been the subject of many academic investigations. Existing state-of-the-art avatar models, such as those in the SMPL family, can correctly depict different human body forms in realistic positions.

Accurately recording, interpreting, and creating non-verbal signals may greatly enhance the realism of avatars in telepresence, augmented reality (AR), and ...

All Credit For This Research Goes To the Researchers on This Project. [ETH](https://nnn.ng/tag/eth/) Zurich and Microsoft researchers. Researchers augment the geometry and deformation fields with a texture network conditioned by position, facial expression, geometry, and the deformed surface’s normals to capture the avatar’s look with high-frequency details. The study’s authors define three distinct neural fields: one for modeling geometry using an implicit occupancy network, another for modeling deformation using learned forward linear blend skinning ( Researchers present unique part-aware sampling and initialization algorithms to train the neural shape and deformation fields effectively. Still, they are limited by the mesh-based representations they use and the quality of the 3D mesh.

Avatars are helpful for stroke, cerebral palsy, and autism. Avatars help mental health by removing barriers to self and social exploration. mimgirl ...

Autism Res 2022, 15 (9), 1603–1608. (15) Cross, L.; Piovesan, A.; Atherton, G. (12) Liu, L. (11) Liu, L. Sci Rep 2017, 7 (1), 9637. Media Psychology 2009, 12 (1), 1–25. Games Health J 2014, 3 (1), 40–48. (4) Li, B. [personality](https://www.psychologytoday.com/gb/basics/personality) as that same display device. Games Health J 2020, 9 (2), 85–94. (1) Biocca, F. [Social learning theory](https://www.psychologytoday.com/gb/basics/social-learning-theory) teaches us that avatars are a powerful environmental stimulus when they become part of our exposome.