AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The implicit representation fusion network (IRFNet) is proposed, a novel framework for precise facial geometry reconstruction that mitigates the geometric distortions inherent in explicit representations and better adapts to diverse facial morphologies and expression variations.
Three-dimensional (3D) face reconstruction from a single two-dimensional (2D) image enables the creation of personalized 3D avatars that preserve facial identity for immersive applications in the metaverse. This thesis aims to develop a realistic 3D human avatar generation framework for the metaverse platform MANGOs (M...
A deep learning framework is proposed to directly predict re-topologized facial meshes from synthetic multiview images generated with Visage Craft, an in-house physically based rendering system using an Appearance 3D Morphable Model (A3DMM).
Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modeling of both non-rigid facial motion and illumination-dependent appearance. Existing Gaussian avatar methods commonly rely on globally coupled representations, in which Gau...
An occlusion-aware hybrid biometric framework for reliable 3D face recognition that reaches an accuracy of up to 98.7%, even in partial occlusions, and significantly reduces the Equal Error Rate, demonstrating its effectiveness and suitability for real-world biometric authentication applications.
This work proposes RAGMesh, a retrieval-augmented framework that leverages text-correlated geometric priors to improve high-fidelity facial synthesis and editing, and introduces Adaptive RAG-guided Supervision (AdaRAGS), a region-aware constraint that explicitly aligns textual semantics with corresponding facial region...
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