{"url":"/dataset/dmo","name":"DMO","full_name":null,"description_markdown":"A large scale dataset to pre-train optical flow prediction network. The data are generated from the DAVIS videos using as-rigid-as-possible principle from Deep-matching and MaskRCNN. The dataset has shown better performance compared to the FlyingChairs dataset.","description_withheld":null,"homepage":"https://github.com/lhoangan/arap_flow","introduced_date":"2018-12-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/unsupervised-generation-of-optical-flow","title":"Automatic Generation of Dense Non-rigid Optical Flow","first_author":"Hoàng-Ân Lê","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["DMO"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}