Papers › Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

14 Jul 2023ICCV 2023 1arXiv:2307.07205archive 2025-07-28

Alessandro Flaborea, Luca Collorone, Guido D'Amely, Stefano D'arrigo, Bardh Prenkaj, Fabio Galasso

Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies. But normalcy shares the same openset'ness property since humans can perform the same action in several ways, which the leading techniques neglect. We propose a novel generative model for video anomaly detection (VAD), which assumes that both normality and abnormality are multimodal. We consider skeletal representations and leverage state-of-the-art diffusion probabilistic models to generate multimodal future human poses. We contribute a novel conditioning on the past motion of people and exploit the improved mode coverage capabilities of diffusion processes to generate different-but-plausible future motions. Upon the statistical aggregation of future modes, an anomaly is detected when the generated set of motions is not pertinent to the actual future. We validate our model on 4 established benchmarks: UBnormal, HR-UBnormal, HR-STC, and HR-Avenue, with extensive experiments surpassing state-of-the-art results.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2307.07205")

Code

Syntology Ran 6 of 8 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 6 ran with no contract checked.

By repository: official repository: 8 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

aleflabo/MoCoDAD officialmentioned in papermentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

8 samples harvested; 6 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

6ran
2unverified

Licence: 0 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from aleflabo/MoCoDAD. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

adjust_lr aleflabo/MoCoDAD/utils/model_utils.py official repository ran MIT (permissive) · 935b328e2a4ca4dd · report
calc_reg_loss aleflabo/MoCoDAD/utils/model_utils.py official repository ran MIT (permissive) · e8961ce4b45db673 · report
compute_bounding_box aleflabo/MoCoDAD/utils/data.py official repository ran MIT (permissive) · 1aaf243c5f555511 · report
create_experiment_dirs aleflabo/MoCoDAD/utils/argparser.py official repository ran MIT (permissive) · d7470d4a55a861f0 · report
extract_global_features aleflabo/MoCoDAD/utils/data.py official repository ran MIT (permissive) · 07a93aced16ac5c7 · report
init_args aleflabo/MoCoDAD/utils/argparser.py official repository ran MIT (permissive) · 2e4def30e7a3be32 · report
get_optim_and_scheduler aleflabo/MoCoDAD/utils/model_utils.py official repository unverified MIT (permissive) · 7e0144cbf00fd665 · report
load_trajectories aleflabo/MoCoDAD/utils/data.py official repository unverified MIT (permissive) · f7e3b54a8686d2b2 · report

Tasks

2D Human Pose EstimationAnomaly DetectionHuman Pose ForecastingOne-Class ClassificationVideo Anomaly Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection UBnormal MoCoDAD AUC 68.3% #6 of 14 Archive leaderboard report
Video Anomaly Detection HR-Avenue MoCoDAD AUC 89.0 #2 of 11 Archive leaderboard report
Video Anomaly Detection HR-ShanghaiTech MoCoDAD AUC 77.6 #5 of 14 Archive leaderboard report
Video Anomaly Detection HR-UBnormal MoCoDAD AUC 68.4 #1 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections