ClipFace: Text-guided Editing of Textured 3D Morphable Models

• URL: http://arxiv.org/abs/2212.01406v2
• Date: Mon, 24 Apr 2023 11:51:40 GMT
• Title: ClipFace: Text-guided Editing of Textured 3D Morphable Models
• Authors: Shivangi Aneja, Justus Thies, Angela Dai, Matthias Nie{\ss}ner
• Abstract summary: We propose ClipFace, a novel self-supervised approach for text-guided editing of textured 3D morphable model of faces. We employ user-friendly language prompts to enable control of the expressions as well as appearance of 3D faces. Our model is trained in a self-supervised fashion by exploiting differentiable rendering and losses based on a pre-trained CLIP model.
• Score: 33.83015491013442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose ClipFace, a novel self-supervised approach for text-guided editing of textured 3D morphable model of faces. Specifically, we employ user-friendly language prompts to enable control of the expressions as well as appearance of 3D faces. We leverage the geometric expressiveness of 3D morphable models, which inherently possess limited controllability and texture expressivity, and develop a self-supervised generative model to jointly synthesize expressive, textured, and articulated faces in 3D. We enable high-quality texture generation for 3D faces by adversarial self-supervised training, guided by differentiable rendering against collections of real RGB images. Controllable editing and manipulation are given by language prompts to adapt texture and expression of the 3D morphable model. To this end, we propose a neural network that predicts both texture and expression latent codes of the morphable model. Our model is trained in a self-supervised fashion by exploiting differentiable rendering and losses based on a pre-trained CLIP model. Once trained, our model jointly predicts face textures in UV-space, along with expression parameters to capture both geometry and texture changes in facial expressions in a single forward pass. We further show the applicability of our method to generate temporally changing textures for a given animation sequence.

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