{"id":"https://openalex.org/W7162304427","doi":"https://doi.org/10.48550/arxiv.2605.23845","title":"Learning a Particle Dynamics Model with Real-world Videos","display_name":"Learning a Particle Dynamics Model with Real-world Videos","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162304427","doi":"https://doi.org/10.48550/arxiv.2605.23845"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23845","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23845","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.2605.23845","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136951668","display_name":"Chanho Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Chanho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136902696","display_name":"Suhas V. Sumukh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sumukh, Suhas V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136954733","display_name":"Li Fuxin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fuxin, Li","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":0.12110000103712082,"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":0.12110000103712082,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.10930000245571136,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.07729999721050262,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/rendering","display_name":"Rendering (computer graphics)","score":0.6062999963760376},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5004000067710876},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4458000063896179},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4075999855995178},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.400299996137619},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3919000029563904},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.39010000228881836},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.3894999921321869},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3831999897956848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7182999849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7056999802589417},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6062999963760376},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47350001335144043},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38920000195503235},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3831999897956848},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.33880001306533813},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23845","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23845","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.2605.23845","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23845","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data-driven":[0],"learning":[1,164],"approaches":[2],"for":[3,107],"physics":[4,19],"simulation,":[5],"sometimes":[6],"referred":[7],"to":[8,17,22,98,121],"as":[9,14,63],"world":[10],"models,":[11],"have":[12],"emerged":[13],"promising":[15],"alternatives":[16],"traditional":[18],"simulators":[20],"due":[21],"their":[23,85,149],"differentiable":[24],"nature.":[25],"Prior":[26],"work":[27],"has":[28],"demonstrated":[29],"impressive":[30],"results":[31],"in":[32,41,54,75],"predicting":[33],"the":[34,88],"motions":[35],"of":[36,198],"rigid":[37],"and":[38,68,145,147,151],"non-rigid":[39],"objects":[40],"complex":[42],"scenes":[43],"involving":[44],"multiple":[45],"interacting":[46],"bodies.":[47],"However,":[48],"these":[49,100],"models":[50,112],"are":[51],"typically":[52],"trained":[53,159],"simulated":[55],"environments":[56],"because":[57],"obtaining":[58],"perfect":[59],"state":[60],"information":[61],"such":[62],"complete":[64],"scene":[65],"point":[66,69],"clouds":[67],"correspondences":[70],"over":[71,154],"time":[72],"is":[73,91,158],"challenging":[74],"real-world":[76,116,166,195],"settings.":[77],"This":[78],"reliance":[79],"on":[80,135,177,182],"synthetic":[81],"data":[82],"can":[83],"limit":[84],"applicability":[86],"when":[87],"sim-to-real":[89],"gap":[90],"large.":[92],"In":[93],"this":[94,189],"work,":[95],"we":[96,119,191],"aim":[97],"overcome":[99],"limitations":[101],"by":[102],"introducing":[103],"a":[104,123,129,194],"novel":[105],"framework":[106],"training":[108],"neural":[109],"object":[110,204],"dynamics":[111,125],"directly":[113,176],"from":[114,139,165],"unlabeled":[115],"videos.":[117],"Specifically,":[118],"propose":[120],"learn":[122],"particle-based":[124],"model":[126,157,174],"compatible":[127],"with":[128,143],"Gaussian":[130],"splatting":[131],"framework,":[132],"which":[133],"operates":[134,175],"dense":[136,178],"particles":[137,142],"derived":[138],"Gaussians":[140,179],"(i.e.,":[141],"scales":[144],"rotations)":[146],"predicts":[148],"position":[150],"rotation":[152],"changes":[153],"time.":[155],"The":[156],"via":[160],"rendering":[161],"supervision,":[162],"enabling":[163],"videos":[167,201],"without":[168,180],"requiring":[169],"particle-level":[170],"labeled":[171],"states.":[172],"Our":[173],"relying":[181],"heuristic":[183],"subsampling":[184],"anchor":[185],"points.":[186],"To":[187],"enable":[188],"study,":[190],"also":[192],"present":[193],"dataset":[196],"consisting":[197],"about":[199],"500":[200],"capturing":[202],"diverse":[203],"interactions.":[205]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-26T00:00:00"}
