TL;DR
Sketch-text-driven rectified flow (STDRF), a conditional rectified-flow framework for identity-preserving and semantically controllable 4D face generation, and a sketch encoder enhanced by Geometric Contour and Texture Detail preprocessing and MixStyle domain adaptation are proposed.
Sketch-to-Face, a cross-modal diffusion-based model that leverages Stable Diffusion to generate photorealistic faces using sketch-based inpainting, is introduced, demonstrating a favorable quality–efficiency trade-off compared with text-and-sketch diffusion baselines.
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...
The proposed sketch-guided face generation pipeline based on a Conditional Variational Autoencoder designed to generate facial reconstructions from sketches and conditional attributes uses a stochastic preprocessing pipeline to extract edge maps from facial photographs, reducing dependence on manually paired sketch-pho...
This work shows that identity-conditioned face synthesis can be performed at a substantially lower computational cost by a latent Consistency Model with few iterations, without compromising image quality for large-scale synthetic face generation.
TopoRig is presented, a topology-agnostic facial rigging framework that predicts FACS-conditioned deformations directly on input mesh vertices while preserving the original topology and amortizes heterogeneous and imperfect expression supervision into a single topology-preserving deformation model.
This work proposes EC\textsuperscript{2}Face, a multimodal face synthesis framework that improves semantic alignment through explicit semantic supervision and distribution-aware optimization, and introduces Explicit Conditional Consistency Guidance (ECCG), which imposes direct consistency supervision in pixel space.
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