{"id":"https://openalex.org/W7161017290","doi":"https://doi.org/10.48550/arxiv.2605.11869","title":"FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity","display_name":"FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161017290","doi":"https://doi.org/10.48550/arxiv.2605.11869"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11869","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":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.2605.11869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136045127","display_name":"Jian Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100842311","display_name":"Jiawei Fan","orcid":"https://orcid.org/0009-0008-7959-2606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136086641","display_name":"Qingbin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qingbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136038816","display_name":"Zheng Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Zheng","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":0.5496000051498413,"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":0.5496000051498413,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.09369999915361404,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11165","display_name":"Image and Video Quality Assessment","score":0.05790000036358833,"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/inference","display_name":"Inference","score":0.7246999740600586},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6485000252723694},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5516999959945679},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5005999803543091},{"id":"https://openalex.org/keywords/inter-frame","display_name":"Inter frame","score":0.4819999933242798},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4596000015735626},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.44339999556541443},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3878999948501587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796000242233276},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7246999740600586},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6485000252723694},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5516999959945679},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5005999803543091},{"id":"https://openalex.org/C39394851","wikidata":"https://www.wikidata.org/wiki/Q921594","display_name":"Inter frame","level":4,"score":0.4819999933242798},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4596000015735626},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40529999136924744},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3817000091075897},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2825999855995178},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27950000762939453},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11869","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":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.2605.11869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11869","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":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":{"While":[0],"the":[1,31,52,88,92,96,111,133,155],"overall":[2],"inference":[3,18],"latency":[4,19],"of":[5,54,113],"Video":[6],"Diffusion":[7],"Transformers":[8],"(DiTs)":[9],"can":[10],"be":[11],"substantially":[12],"reduced":[13,118],"through":[14],"model":[15,156],"distillation,":[16],"per-step":[17],"remains":[20,129],"a":[21,37,65,81,122,191],"critical":[22],"bottleneck.":[23],"Existing":[24],"acceleration":[25],"paradigms":[26],"primarily":[27],"exploit":[28],"redundancy":[29],"across":[30,154,185],"denoising":[32],"trajectory;":[33],"however,":[34],"we":[35,74,140],"identify":[36],"limitation":[38],"where":[39,125],"these":[40],"step-wise":[41],"strategies":[42],"encounter":[43],"diminishing":[44],"returns":[45],"in":[46],"few-step":[47],"regimes.":[48],"In":[49],"such":[50],"scenarios,":[51],"scarcity":[53],"temporal":[55,93],"states":[56],"prevents":[57],"effective":[58],"feature":[59],"reuse":[60],"or":[61],"predictive":[62],"modeling,":[63],"creating":[64],"formidable":[66],"barrier":[67],"to":[68,95,132],"further":[69],"acceleration.":[70],"To":[71],"overcome":[72],"this,":[73],"propose":[75],"Frame":[76,142],"Interleaved":[77,143],"Sparsity":[78,144],"DiT":[79],"(FIS-DiT),":[80],"training-free":[82],"and":[83,172,187,193],"operator-agnostic":[84],"framework":[85],"that":[86,116,150,176],"shifts":[87],"optimization":[89],"focus":[90],"from":[91],"trajectory":[94],"latent":[97,160],"frame":[98,127,152],"dimension.":[99],"Our":[100],"approach":[101],"is":[102],"motivated":[103],"by":[104],"an":[105,147],"intrinsic":[106],"duality":[107],"within":[108],"this":[109,138],"dimension:":[110],"existence":[112],"frame-wise":[114],"sparsity":[115],"permits":[117],"computation,":[119],"coupled":[120],"with":[121,182],"structural":[123],"consistency":[124],"each":[126],"position":[128],"equally":[130],"vital":[131],"global":[134],"spatiotemporal":[135],"context.":[136],"Leveraging":[137],"insight,":[139],"implement":[141],"(FIS)":[145],"as":[146],"execution":[148],"strategy":[149],"manipulates":[151],"subsets":[153],"hierarchy,":[157],"refreshing":[158],"all":[159],"positions":[161],"without":[162],"requiring":[163],"full-scale":[164],"block":[165],"computation.":[166],"Empirical":[167],"evaluations":[168],"on":[169],"Wan":[170],"2.2":[171],"HunyuanVideo":[173],"1.5":[174],"demonstrate":[175],"FIS-DiT":[177],"consistently":[178],"achieves":[179],"2.11--2.41$\\times$":[180],"speedup":[181],"negligible":[183],"degradation":[184],"VBench-Q":[186],"CLIP":[188],"metrics,":[189],"providing":[190],"scalable":[192],"robust":[194],"pathway":[195],"toward":[196],"real-time":[197],"high-definition":[198],"video":[199],"generation.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}
