Enhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data

• URL: http://arxiv.org/abs/2607.04490v1
• Date: Sun, 05 Jul 2026 20:27:18 GMT
• Title: Enhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data
• Abstract summary: We propose a data synthesis framework that generates HMC-view images from frontal-view images.<n>Specifically, we first reconstruct 3D textured meshes from images and then apply a camera system to render images from the HMC perspective.
• Score: 11.540496832054815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial expression recognition (FER) is crucial for social interaction in mixed reality environments that employ head-mounted displays (HMD). However, collecting FER data from head-mounted cameras (HMC) is challenging due to privacy concerns and the diversity of HMD platforms. Moreover, existing FER datasets are not directly applicable due to the unique perspectives of HMCs. The lack of sufficient data hinders the development of neural network-based HMC FER methods. To address data scarcity, we propose a data synthesis framework that generates HMC-view images from frontal-view images, leveraging abundant existing annotated datasets. Specifically, we first reconstruct 3D textured meshes from images and then apply a configurable camera system to render images from the HMC perspective. Additionally, we introduce a texture-space alignment network (TSAN) that enables accurate texture sampling from images to preserve detailed facial expressions. To evaluate the proposed method, we conduct extensive experiments on both simulated and real HMC datasets. Experimental results demonstrate that models trained on our synthetic dataset outperform those trained on existing datasets and exhibit better generalization across different camera configurations.

Related papers

• Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis [3.3176565054468714]
  This paper introduces a novel framework for labour-free segmentation that successfully bridges the simulation-to-reality gap.<n>We employ a Cycle-Consistent Generative Adversarial Network (CycleGAN) for unpaired image-to-image translation.<n>A U-Net model, trained exclusively on this synthetic data, demonstrated remarkable generalisation when deployed on unseen experimental images.
  arXiv  Detail & Related papers  (2026-02-02T06:36:06Z)
• MRGen: Segmentation Data Engine for Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically important imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize data, for training segmentation models for underrepresented modalities.<n>We present MRGen, a data engine for controllable medical image synthesis conditioned on text prompts and segmentation masks.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
  Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
  arXiv  Detail & Related papers  (2024-07-29T18:00:10Z)
• Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes [2.8498944632323755]
  We propose an end-to-end hybrid architecture for medical image segmentation. We use Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images. Our architecture operates on a slice-by-slice basis to segment 3D volumes, capitilizing on the richly augmented dataset.
  arXiv  Detail & Related papers  (2024-06-17T15:42:08Z)
• Diffusion Models are Efficient Data Generators for Human Mesh Recovery [55.37787289869703]
  We show that synthetic data created by generative models is complementary to CG-rendered data.<n>We propose an effective data generation pipeline based on recent diffusion models, termed HumanWild.<n>Our work could pave the way for scaling up 3D human recovery to in-the-wild scenes.
  arXiv  Detail & Related papers  (2024-03-17T06:31:16Z)
• MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition [11.820043444385432]
  We introduce a novel FER training paradigm named Mask Image pre-training with MIx Contrastive fine-tuning (MIMIC) In the initial phase, we pre-train the ViT via masked image reconstruction on general images. In the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive range of positive samples.
  arXiv  Detail & Related papers  (2024-01-14T10:30:32Z)
• Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach [1.3359609092684614]
  In recent years, photogrammetry has been widely used in many areas to create 3D virtual data representing the physical environment. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations.
  arXiv  Detail & Related papers  (2021-09-24T22:29:26Z)
• From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data [58.50411487497146]
  We propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing.
  arXiv  Detail & Related papers  (2021-08-06T04:00:28Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation [75.59415852802958]
  Shape-My-Face (SMF) is a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model. Our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets.
  arXiv  Detail & Related papers  (2020-12-16T20:02:36Z)
• Methodology for Building Synthetic Datasets with Virtual Humans [1.5556923898855324]
  Large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities.
  arXiv  Detail & Related papers  (2020-06-21T10:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.