ImFace++：一种基于隐式神经表示的复杂非线性3D可变形人脸模型
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
AI总结：
本文提出基于隐式神经表示的非线性3D可变形人脸模型ImFace++，通过解耦身份与表情形变场、细化位移场及Neural Blend-Field，并结合扩展表情嵌入的学习策略，显著提升了人脸重建保真度与对应精度。
摘要：
3D人脸的准确表示在各种计算机视觉和图形学应用中至关重要。然而，由于数据离散化和模型线性带来的限制，当前研究在精确捕捉身份和表情线索方面仍面临挑战。本文提出了一种名为ImFace++的新型3D可变形人脸模型，旨在通过隐式神经表示学习一个复杂且连续的空间。ImFace++首先构建两个显式解耦的形变场，分别对与身份和表情相关的复杂形状进行建模，这同时促进了跨不同人脸形状点对点对应关系的自动学习。为捕捉更复杂的人脸细节，模板空间内进一步引入了细化位移场，实现了对个体特定人脸细节的细粒度学习。此外，设计了Neural Blend-Field，通过自适应混合一组局部场来增强表示能力。除ImFace++外，还设计了一种改进的学习策略来扩展表情嵌入，允许更广泛的表情变化。全面的定性和定量评估表明，ImFace++在人脸重建保真度和对应精度方面均显著推进了当前的技术水平。
英文摘要：
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisticated and continuous space with implicit neural representations. ImFace++ first constructs two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, which simultaneously facilitate automatic learning of point-to-point correspondences across diverse facial shapes. To capture more sophisticated facial details, a refinement displacement field within the template space is further incorporated, enabling fine-grained learning of individual-specific facial details. Furthermore, a Neural Blend-Field is designed to reinforce the representation capabilities through adaptive blending of an array of local fields. In addition to ImFace++, we devise an improved learning strategy to extend expression embeddings, allowing for a broader range of expression variations. Comprehensive qualitative and quantitative evaluation demonstrates that ImFace++ significantly advances the state-of-the-art in terms of both face reconstruction fidelity and correspondence accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
