{"id":"https://openalex.org/W7162770225","doi":"https://doi.org/10.48550/arxiv.2605.29762","title":"GeoMag: Geometric-Aware Video Motion Magnification via State Space Model","display_name":"GeoMag: Geometric-Aware Video Motion Magnification via State Space Model","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162770225","doi":"https://doi.org/10.48550/arxiv.2605.29762"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29762","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.29762","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137385750","display_name":"Kecheng Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Kecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137312829","display_name":"Yuchen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137369988","display_name":"Bingqing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bingqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135938099","display_name":"Boqiang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Boqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137367927","display_name":"Wenbin Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Wenbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049819376","display_name":"Shiyuan Pei","orcid":"https://orcid.org/0000-0002-1310-6301"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei, Shiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.36410000920295715,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.36410000920295715,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.17470000684261322,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.07270000129938126,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.534500002861023},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4941999912261963},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4650000035762787},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.46470001339912415},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4311000108718872},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.42410001158714294},{"id":"https://openalex.org/keywords/structure-from-motion","display_name":"Structure from motion","score":0.40799999237060547},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.37880000472068787}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722599983215332},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6470999717712402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6256999969482422},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.534500002861023},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C146159030","wikidata":"https://www.wikidata.org/wiki/Q7625099","display_name":"Structure from motion","level":3,"score":0.40799999237060547},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3571999967098236},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.3357999920845032},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29910001158714294},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C32990609","wikidata":"https://www.wikidata.org/wiki/Q306542","display_name":"Transformation geometry","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29762","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.29762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29762","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"Motion":[1],"Magnification":[2],"(VMM)":[3],"reveals":[4],"imperceptible":[5],"dynamics":[6],"but":[7],"often":[8],"suffers":[9],"from":[10],"structural":[11,137],"inconsistencies":[12],"under":[13],"complex":[14],"geometric":[15,53,97],"transformations.":[16],"Existing":[17],"learning-based":[18],"methods":[19,124],"generally":[20],"face":[21],"a":[22,68,90],"trade-off":[23],"between":[24],"the":[25,32,52,104],"limited":[26],"global":[27],"context":[28],"of":[29,36,108],"CNNs":[30],"and":[31,54,106,115,128,135],"high":[33],"computational":[34,129],"cost":[35],"Transformers.":[37],"In":[38],"addition,":[39],"current":[40],"training":[41,109],"protocols,":[42],"largely":[43],"dominated":[44],"by":[45],"simple":[46],"linear":[47,84],"motion,":[48],"fail":[49],"to":[50,77],"capture":[51],"imaging":[55],"complexities":[56],"encountered":[57],"in":[58,125],"real-world":[59,116],"videos.":[60],"To":[61],"address":[62],"these":[63],"issues,":[64],"we":[65],"propose":[66],"GeoMag,":[67],"geometric-aware":[69],"VMM":[70],"framework":[71],"built":[72],"upon":[73],"State":[74],"Space":[75],"Models":[76],"achieve":[78],"globally":[79],"consistent":[80],"motion":[81],"amplification":[82],"with":[83,100],"complexity.":[85],"We":[86],"further":[87],"construct":[88],"Geo-200K,":[89],"large-scale":[91],"synthetic":[92,114],"dataset":[93],"that":[94,119],"introduces":[95],"rich":[96],"transformations":[98],"together":[99],"sensor-realistic":[101],"degradations,":[102],"improving":[103],"diversity":[105],"realism":[107],"signals.":[110],"Extensive":[111],"experiments":[112],"on":[113],"benchmarks":[117],"show":[118],"GeoMag":[120],"consistently":[121],"outperforms":[122],"prior":[123],"visual":[126],"fidelity":[127],"efficiency,":[130],"while":[131],"producing":[132],"fewer":[133],"artifacts":[134],"better":[136],"consistency.":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}