Towards Multi-domain Face Landmark Detection with Synthetic Data from Diffusion model

• URL: http://arxiv.org/abs/2401.13191v1
• Date: Wed, 24 Jan 2024 02:35:32 GMT
• Title: Towards Multi-domain Face Landmark Detection with Synthetic Data from Diffusion model
• Authors: Yuanming Li, Gwantae Kim, Jeong-gi Kwak, Bon-hwa Ku, Hanseok Ko
• Abstract summary: deep learning-based facial landmark detection for in-the-wild faces has achieved significant improvement. There are still challenges in face landmark detection in other domains (e.g. cartoon, caricature, etc) We design a two-stage training approach that effectively leverages limited datasets and the pre-trained diffusion model. Our results demonstrate that our method outperforms existing methods on multi-domain face landmark detection.
• Score: 27.307563102526192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning-based facial landmark detection for in-the-wild faces has achieved significant improvement. However, there are still challenges in face landmark detection in other domains (e.g. cartoon, caricature, etc). This is due to the scarcity of extensively annotated training data. To tackle this concern, we design a two-stage training approach that effectively leverages limited datasets and the pre-trained diffusion model to obtain aligned pairs of landmarks and face in multiple domains. In the first stage, we train a landmark-conditioned face generation model on a large dataset of real faces. In the second stage, we fine-tune the above model on a small dataset of image-landmark pairs with text prompts for controlling the domain. Our new designs enable our method to generate high-quality synthetic paired datasets from multiple domains while preserving the alignment between landmarks and facial features. Finally, we fine-tuned a pre-trained face landmark detection model on the synthetic dataset to achieve multi-domain face landmark detection. Our qualitative and quantitative results demonstrate that our method outperforms existing methods on multi-domain face landmark detection.

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