{"id":"https://openalex.org/W7166726650","doi":"https://doi.org/10.48550/arxiv.2606.28677","title":"SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending","display_name":"SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending","publication_year":2026,"publication_date":"2026-06-27","ids":{"openalex":"https://openalex.org/W7166726650","doi":"https://doi.org/10.48550/arxiv.2606.28677"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28677","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.2606.28677","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139688542","display_name":"Haoran Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139678239","display_name":"Xiaoxu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiaoxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139662634","display_name":"Xiaoyu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065256379","display_name":"Zongsheng Yue","orcid":"https://orcid.org/0000-0002-9178-671X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Zongsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052379142","display_name":"Sibin Deng","orcid":"https://orcid.org/0009-0000-5622-6955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Sibin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139655713","display_name":"Wangmeng Zuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Wangmeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139667449","display_name":"Ying Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ying","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":0.7728000283241272,"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":0.7728000283241272,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.04270000010728836,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.041999999433755875,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7480000257492065},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5964999794960022},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5541999936103821},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.4885999858379364},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.44609999656677246},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.423799991607666},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.3971000015735626}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7480000257492065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7127000093460083},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5964999794960022},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5541999936103821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49950000643730164},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3691999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32120001316070557},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28677","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.2606.28677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28677","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],"diffusion":[1],"models":[2],"excel":[3],"in":[4],"video":[5],"restoration,":[6],"their":[7],"reliance":[8],"on":[9,99,166],"extensive":[10],"iterative":[11],"steps":[12],"limits":[13],"efficiency.":[14],"Conversely,":[15],"aggressive":[16],"single-step":[17],"distillation":[18],"often":[19],"compromises":[20],"fine":[21],"texture":[22],"recovery.":[23],"To":[24,68],"achieve":[25],"an":[26,41],"optimal":[27],"balance,":[28],"we":[29,71,122],"present":[30],"SATB-VR,":[31],"a":[32,64,124,135],"few-step":[33,153],"paradigm":[34],"that":[35],"jump-starts":[36],"the":[37,57,60,73,80,85,91,95,100,104,117,130],"denoising":[38],"process":[39],"via":[40],"auxiliary":[42],"predictor,":[43],"explicitly":[44,139],"bypassing":[45],"early":[46],"low":[47],"signal-to-noise":[48],"ratio":[49],"(SNR)":[50],"steps.":[51],"However,":[52],"naive":[53],"joint":[54],"training":[55],"of":[56],"predictor":[58,145],"and":[59,143,169],"denoiser":[61,105,133],"inherently":[62],"introduces":[63],"severe":[65],"train-inference":[66],"discrepancy.":[67],"resolve":[69],"this,":[70],"propose":[72],"SNR-Aware":[74],"Trajectory":[75],"Blending":[76],"(SATB)":[77],"strategy.":[78],"During":[79],"forward":[81],"process,":[82],"SATB":[83],"constructs":[84],"noisy":[86],"input":[87],"by":[88],"dynamically":[89],"blending":[90],"predictor's":[92],"output":[93],"with":[94],"ground-truth":[96],"trajectory":[97],"based":[98],"SNRs.":[101],"This":[102],"forces":[103],"to":[106,116,138],"robustly":[107],"compensate":[108],"for":[109],"initial":[110],"prediction":[111],"errors":[112],"while":[113],"smoothly":[114],"converging":[115],"clean":[118],"data":[119],"manifold.":[120],"Furthermore,":[121],"introduce":[123],"Denoiser-Driven":[125],"Consistency":[126],"(DDC)":[127],"loss,":[128],"leveraging":[129],"concurrently":[131],"updated":[132],"as":[134],"dynamic":[136],"evaluator":[137],"align":[140],"internal":[141],"features":[142],"boost":[144],"accuracy.":[146],"Extensive":[147],"experiments":[148],"demonstrate":[149],"that,":[150],"under":[151],"flexible":[152],"inference":[154],"regimes":[155],"(\\eg,":[156],"$\\le":[157],"5$":[158],"steps),":[159],"SATB-VR":[160],"performs":[161],"favorably":[162],"against":[163],"existing":[164],"approaches":[165],"synthetic,":[167],"real-world,":[168],"AIGC":[170],"benchmarks.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}