Causal Representation Learning for Context-Aware Face Transfer
面向情境感知人脸迁移的因果表征学习AI 解读 ↓
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面向情境感知人脸迁移的因果表征学习
- 研究问题
- 研究如何在人脸迁移中保留身份及身份相关脸形，同时适应训练观察未充分覆盖的表情、头部姿态与背景情境。
- 方法与思路
- 作者提出 CarTrans，通过受控干预与反事实推断估计表情和姿态效应，并用核回归编码器分离目标图像中的身份与情境信息。
- 材料陈述的贡献
- 摘要称，该方法将情境因素的因果效应纳入人脸表征，减少对姿态与表情空间大量观测的需求，并支持细粒度外观控制。
- 阅读关注点
- 阅读全文时应核对干预如何构造、反事实效应如何识别，以及身份信息分离和未见情境下表现的评价指标与比较设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明因果识别条件、实验规模及具体性能数值。
原文摘要
Human face synthesis involves transferring knowledge about the identity and identity-dependent face shape (IDFS) of a human face to target face images where the context (e.g., facial expressions, head poses, and other background factors) may change dramatically. Human faces are non-rigid, so facial expression leads to deformation of face shape, and head pose also affects the face observed in 2D images. A key challenge in face transfer is to match the face with unobserved new contexts, adapting the face appearance to different poses and expressions accordingly. In this work, we find a way to provide prior knowledge for generative models to reason about the appropriate appearance of a human face in response to various expressions and poses. We propose a novel context-aware face transfer method, called CarTrans, that incorporates causal effects of contextual factors into face representation, and thus is able to be aware of the uncertainty of new contexts. We estimate the effect of facial expression and head pose in terms of counterfactual inference by designing a controlled intervention trial, thus avoiding the requirement of a large number of observations to cover the pose-expression space well. Moreover, we propose a kernel regression-based encoder that eliminates the identity specificity of target faces when encoding contextual information from target images. The resulting method shows impressive performance, allowing fine-grained control over face shape and appearance under various contextual conditions.