SRFlow: A Dataset and Regularization Model for High-Resolution Facial Optical Flow via Splatting Rasterization

• URL: http://arxiv.org/abs/2601.06479v1
• Date: Sat, 10 Jan 2026 08:14:00 GMT
• Title: SRFlow: A Dataset and Regularization Model for High-Resolution Facial Optical Flow via Splatting Rasterization
• Authors: JiaLin Zhang, Dong Li,
• Abstract summary: We introduce Splatting Rasterization Flow (SRFlow), a high-resolution facial optical flow dataset.<n>We also present SRFlowNet, a facial optical flow model with tailored regularization losses.
• Score: 9.108257371079274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial optical flow supports a wide range of tasks in facial motion analysis. However, the lack of high-resolution facial optical flow datasets has hindered progress in this area. In this paper, we introduce Splatting Rasterization Flow (SRFlow), a high-resolution facial optical flow dataset, and Splatting Rasterization Guided FlowNet (SRFlowNet), a facial optical flow model with tailored regularization losses. These losses constrain flow predictions using masks and gradients computed via difference or Sobel operator. This effectively suppresses high-frequency noise and large-scale errors in texture-less or repetitive-pattern regions, enabling SRFlowNet to be the first model explicitly capable of capturing high-resolution skin motion guided by Gaussian splatting rasterization. Experiments show that training with the SRFlow dataset improves facial optical flow estimation across various optical flow models, reducing end-point error (EPE) by up to 42% (from 0.5081 to 0.2953). Furthermore, when coupled with the SRFlow dataset, SRFlowNet achieves up to a 48% improvement in F1-score (from 0.4733 to 0.6947) on a composite of three micro-expression datasets. These results demonstrate the value of advancing both facial optical flow estimation and micro-expression recognition.

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