{"id":"https://openalex.org/W7162301472","doi":"https://doi.org/10.48550/arxiv.2605.23458","title":"One-Forcing: Towards Stable One-Step Autoregressive Video Generation","display_name":"One-Forcing: Towards Stable One-Step Autoregressive Video Generation","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162301472","doi":"https://doi.org/10.48550/arxiv.2605.23458"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2605.23458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136933311","display_name":"Jiaqi Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136965979","display_name":"Justin Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Justin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123850992","display_name":"Yuanhao Ban","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ban, Yuanhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136902976","display_name":"Cho-Jui Hsieh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsieh, Cho-Jui","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8981000185012817,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8981000185012817,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.03290000185370445,"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/T11019","display_name":"Image Enhancement Techniques","score":0.012199999764561653,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8047000169754028},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4154999852180481},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.41179999709129333},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.3176000118255615},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.3125999867916107},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.28290000557899475}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8047000169754028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988999843597412},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4154999852180481},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3828999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33160001039505005},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3061000108718872},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3000999987125397},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C2992829110","wikidata":"https://www.wikidata.org/wiki/Q3921615","display_name":"First generation","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26109999418258667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.23458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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":{"Recent":[0],"advances":[1],"have":[2,167],"substantially":[3],"improved":[4],"real-time":[5],"interactive":[6],"video":[7,18,109,129],"generation":[8,19,130,146],"in":[9,57],"the":[10,48,58,96,155,159],"autoregressive":[11,17,145],"regime.":[12],"However,":[13],"most":[14],"existing":[15],"few-step":[16],"methods,":[20],"often":[21,65],"distilled":[22],"from":[23,43],"a":[24,30,89,118,162],"corresponding":[25],"many-step":[26,137],"teacher,":[27],"default":[28],"to":[29,78,169],"4-step":[31],"sampling":[32,51],"configuration,":[33],"which":[34,94],"still":[35],"incurs":[36],"considerable":[37],"latency":[38],"during":[39],"deployment":[40],"and":[41,106,132],"suffers":[42],"severe":[44],"quality":[45],"degradation":[46],"when":[47],"number":[49],"of":[50,121,154,158],"steps":[52],"is":[53],"further":[54,140],"reduced,":[55],"particularly":[56],"one-step":[59,108,127,143],"setting.":[60],"Trajectory-style":[61],"consistency":[62],"distillation":[63],"methods":[64,131,166],"produce":[66],"videos":[67],"with":[68,99,135,151],"weak":[69],"dynamics,":[70],"while":[71],"DMD-based":[72],"approaches,":[73],"such":[74],"as":[75],"Self-Forcing,":[76],"tend":[77],"yield":[79],"blurry":[80],"frames.":[81],"To":[82],"address":[83],"this":[84],"challenge,":[85],"we":[86],"propose":[87],"One-Forcing,":[88],"simple":[90],"yet":[91],"effective":[92],"approach":[93],"augments":[95],"DMD":[97],"objective":[98],"an":[100],"auxiliary":[101],"GAN":[102],"loss":[103],"for":[104],"high-quality":[105],"efficient":[107],"generation.":[110],"Experiments":[111],"on":[112],"VBench":[113],"show":[114],"that":[115,142,164],"One-Forcing":[116],"achieves":[117],"total":[119],"score":[120],"83.76,":[122],"establishing":[123],"state-of-the-art":[124],"performance":[125],"among":[126],"causal":[128],"remaining":[133],"competitive":[134],"strong":[136],"approaches.":[138],"We":[139],"demonstrate":[141],"framewise":[144],"can":[147],"be":[148],"achieved":[149],"stably":[150],"merely":[152],"one-third":[153],"training":[156],"cost":[157],"chunkwise":[160],"model,":[161],"setting":[163],"prior":[165],"failed":[168],"achieve":[170],"successfully.":[171]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-26T00:00:00"}