Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable identity. However, current identity encoders, which are typically trained and calibrated on natural photographs, exhibit severe brittleness under stylization. They often mistake changes in texture or color palette for identity drift or fail to detect geometric exaggerations. This reveals the lack of a style-agnostic framework to evaluate and supervise identity consistency across varying styles and strengths. To address this gap, we introduce StyleID, a human perception-aware dataset and evaluation framework for facial identity under stylization. StyleID comprises two datasets: (i) StyleBench-H, a benchmark that captures human same-different verification judgments across diffusion- and flow-matching-based stylization at multiple style strengths, and (ii) StyleBench-S, a supervision set derived from psychometric recognition-strength curves obtained through controlled two-alternative forced-choice (2AFC) experiments. Leveraging StyleBench-S, we fine-tune existing semantic encoders to align their similarity orderings with human perception across styles and strengths. Experiments demonstrate that our calibrated models yield significantly higher correlation with human judgments and enhanced robustness for out-of-domain, artist drawn portraits. All of our datasets, code, and pretrained models will be publicly available.

Abstract We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user‐defined text descriptions specifying a target style. Building upon a pre‐trained mesh deformation network and a texture generator for original 3DMM‐based realistic human faces, our approach fine‐tunes these models using stylized facial images generated via text‐guided image‐to‐image (i2i) translation with a diffusion model, which serve as stylization targets for the rendered mesh. To prevent undesired changes in identity, facial alignment, or expressions during i2i translation, we introduce a stylization method that explicitly preserves the facial attributes of the source image. By maintaining these critical attributes during image stylization, the proposed approach ensures consistent 3D style transfer across the 3DMM parameter space through image‐based training. Once trained, StyleMM enables feed‐forward generation of stylized face meshes with explicit control over shape, expression, and texture parameters, producing meshes with consistent vertex connectivity and animatability. Quantitative and qualitative evaluations demonstrate that our approach outperforms state‐of‐the‐art methods in terms of identity‐level facial diversity and stylization capability. The code and videos are available at kwanyun.github.io/stylemm_page . Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and Techniques—

A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.