Papers › Seamless Human Motion Composition with Blended Positional Encodings

Seamless Human Motion Composition with Blended Positional Encodings

23 Feb 2024CVPR 2024 1arXiv:2402.15509archive 2025-07-28

German Barquero, Sergio Escalera, Cristina Palmero

Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.

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="2402.15509")

Code

Syntology Ran 8 of 10 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 4 ran with no contract checked.

By repository: official repository: 10 samples from 1 repository, 8 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

BarqueroGerman/FlowMDM officialmentioned on GitHubpytorchNOASSERTION 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

10 samples harvested; 8 ran; 1 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.

1ran · honoured contract
3ran · our draft was wrong
4ran
2unverified

Licence: 10 of the 10 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 BarqueroGerman/FlowMDM. “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.

approx_standard_normal_cdf BarqueroGerman/FlowMDM/diffusion/losses.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · cfd76fd0d89574a4 · report
collate_fn BarqueroGerman/FlowMDM/data_loaders/model_motion_loaders.py official repository ran · our draft was wrong no licence file found · pointer only · a902cf596cea9116 · report
discretized_gaussian_log_likelihood BarqueroGerman/FlowMDM/diffusion/losses.py official repository ran · our draft was wrong no licence file found · pointer only · cd33283d615fb3d7 · report
load_model_wo_clip BarqueroGerman/FlowMDM/utils/model_util.py official repository ran licence not identified · pointer only · 0957bc70a00901f7 · report
normal_kl BarqueroGerman/FlowMDM/diffusion/losses.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · cf2798b666b231ca · report
pad_sample_with_zeros BarqueroGerman/FlowMDM/data_loaders/datasets_composition.py official repository ran fingerprinted licence not identified · pointer only · bcf11599076c0748 · report
standarize_text BarqueroGerman/FlowMDM/data_loaders/amass/babel_flowmdm.py official repository ran fingerprinted licence not identified · pointer only · 95141207b4302e3d · report
wrap_model BarqueroGerman/FlowMDM/model/cfg_sampler.py official repository ran licence not identified · pointer only · abcf1a10add7edd0 · report
get_dataset BarqueroGerman/FlowMDM/data_loaders/get_data.py official repository unverified licence not identified · pointer only · ac9ef5c0598455fa · report
get_dataset_class BarqueroGerman/FlowMDM/data_loaders/get_data.py official repository unverified licence not identified · pointer only · 66afe7bc37ea03da · report

Tasks

DenoisingMotion GenerationMotion SynthesisTemporal Human Motion Composition

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

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