{"id":"https://openalex.org/W7162757298","doi":"https://doi.org/10.48550/arxiv.2605.30352","title":"GMOS: Grounding Moving Object Segmentation in 3D Space and Time","display_name":"GMOS: Grounding Moving Object Segmentation in 3D Space and Time","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162757298","doi":"https://doi.org/10.48550/arxiv.2605.30352"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30352","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30352","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.2605.30352","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101281862","display_name":"Junyu Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Junyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075613881","display_name":"Tengda Han","orcid":"https://orcid.org/0000-0002-1874-9664"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Tengda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137390187","display_name":"Weidi Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Weidi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137310873","display_name":"Andrew Zisserman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zisserman, Andrew","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.30149999260902405,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.30149999260902405,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.29899999499320984,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.2460000067949295,"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/segmentation","display_name":"Segmentation","score":0.7418000102043152},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6243000030517578},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.6069999933242798},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.5722000002861023},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.45969998836517334},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4578000009059906},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4521999955177307}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7900000214576721},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7692999839782715},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7418000102043152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858000159263611},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6243000030517578},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6069999933242798},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5722000002861023},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.45969998836517334},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4578000009059906},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3725000023841858},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.37139999866485596},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C2779020251","wikidata":"https://www.wikidata.org/wiki/Q3555171","display_name":"Motion vector","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3158000111579895},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2904999852180481},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2685999870300293}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30352","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30352","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.2605.30352","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30352","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":{"Moving":[0],"Object":[1,127],"Segmentation":[2,128],"(MOS)":[3],"aims":[4],"to":[5,82],"discover,":[6],"segment,":[7],"and":[8,44,69,71,103,131,153,166],"track":[9],"objects":[10],"that":[11,39,76],"move":[12],"independently":[13],"of":[14,57,88,113],"the":[15,53],"camera.":[16],"Current":[17],"MOS":[18,65,164],"methods,":[19],"however,":[20],"exhibit":[21],"two":[22],"fundamental":[23],"limitations:":[24],"they":[25,45],"rely":[26],"on":[27,79],"pre-computed":[28],"2D":[29],"auxiliary":[30],"modalities":[31],"such":[32],"as":[33,48],"optical":[34],"flow":[35],"or":[36],"point":[37],"trajectories":[38],"lack":[40],"3D":[41,67],"geometric":[42],"information,":[43],"treat":[46],"motion":[47,55,120],"a":[49,74,93,111,137],"sequence-level":[50],"attribute,":[51],"overlooking":[52],"instantaneous":[54],"state":[56],"each":[58],"object.":[59],"We":[60],"address":[61],"both":[62],"by":[63],"grounding":[64],"in":[66,105],"space":[68],"time,":[70],"propose":[72],"GMOS,":[73],"framework":[75],"operates":[77],"directly":[78],"RGB":[80],"video":[81],"produce":[83],"3D-aware,":[84],"temporally":[85,138],"fine-grained":[86,139],"segmentation":[87],"multiple":[89],"moving":[90],"objects,":[91],"alongside":[92],"foreground--background":[94],"variant":[95],"GMOS-S":[96],"for":[97,135,170],"faster":[98,160],"deployment.":[99,172],"To":[100],"support":[101],"training":[102],"evaluation":[104,140],"this":[106],"regime,":[107],"we":[108],"curate":[109],"GMOS-2K,":[110],"dataset":[112],"2,210":[114],"real-world":[115],"videos":[116],"with":[117,142],"per-object":[118],"temporal":[119],"annotations":[121],"drawn":[122],"from":[123],"five":[124],"established":[125],"Video":[126],"(VOS)":[129],"benchmarks,":[130,156],"formalise":[132],"MOS-I":[133],"(\"I\"":[134],"instantaneous),":[136],"protocol":[141],"three":[143],"complementary":[144],"metrics.":[145],"GMOS":[146],"achieves":[147],"state-of-the-art":[148],"results":[149],"across":[150],"MOS,":[151],"MOS-I,":[152],"Unsupervised":[154],"VOS":[155],"while":[157],"running":[158],"significantly":[159],"than":[161],"prior":[162],"multi-object":[163],"methods":[165],"supporting":[167],"online":[168],"inference":[169],"streaming":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}