{
  "annotation_id": "04baec3a-bd57-4b5b-a83a-ea304997fe51",
  "date_created": "2026-02-17T05:53:08.776000Z",
  "date_modified": "2026-02-17T05:53:08.776000Z",
  "file_hash": "b55c80706a14ccc7ea51abda54656ed4823cdaa8e1bbce2b5097f0b57db25af6",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.06479",
    "authors": [
      "JiaLin Zhang",
      "Dong Li"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "SRFlow: A Dataset and Regularization Model for High-Resolution Facial Optical Flow via Splatting Rasterization",
    "url": "https://arxiv.org/abs/2601.06479",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8745f6b9-4fbc-4090-bf83-4c6f06657847",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}