{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/seamless-human-motion-composition-with","title":"Seamless Human Motion Composition with Blended Positional Encodings","arxiv_id":"2402.15509","date":"2024-02-23","proceeding":"CVPR 2024 1","authors":["German Barquero","Sergio Escalera","Cristina Palmero"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2402.15509v1","url_pdf":"https://arxiv.org/pdf/2402.15509v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"seamless-human-motion-composition-with","repo_url":"https://github.com/BarqueroGerman/FlowMDM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"motion-generation","task_name":"Motion Generation"},{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"},{"task_slug":"temporal-human-motion-composition","task_name":"Temporal Human Motion Composition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.15509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15509"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/BarqueroGerman/FlowMDM","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_draft_wrong":3,"ran":4,"ran_honours":1,"unverified":2},"by_repo_kind":{"official":{"samples":10,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"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"},"n_pointer_only_for_licence":10,"samples":[{"code_sha256_prefix":"cfd76fd0d89574a4","entry":"approx_standard_normal_cdf","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"diffusion/losses.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/diffusion/losses.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cfd76fd0d89574a4"}},{"code_sha256_prefix":"a902cf596cea9116","entry":"collate_fn","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"data_loaders/model_motion_loaders.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/data_loaders/model_motion_loaders.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a902cf596cea9116"}},{"code_sha256_prefix":"cd33283d615fb3d7","entry":"discretized_gaussian_log_likelihood","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"diffusion/losses.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/diffusion/losses.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cd33283d615fb3d7"}},{"code_sha256_prefix":"0957bc70a00901f7","entry":"load_model_wo_clip","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"utils/model_util.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/utils/model_util.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"0957bc70a00901f7"}},{"code_sha256_prefix":"cf2798b666b231ca","entry":"normal_kl","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"diffusion/losses.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/diffusion/losses.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cf2798b666b231ca"}},{"code_sha256_prefix":"bcf11599076c0748","entry":"pad_sample_with_zeros","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"data_loaders/datasets_composition.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/data_loaders/datasets_composition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"bcf11599076c0748"}},{"code_sha256_prefix":"95141207b4302e3d","entry":"standarize_text","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"data_loaders/amass/babel_flowmdm.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/data_loaders/amass/babel_flowmdm.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"95141207b4302e3d"}},{"code_sha256_prefix":"abcf1a10add7edd0","entry":"wrap_model","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"model/cfg_sampler.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/model/cfg_sampler.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"abcf1a10add7edd0"}},{"code_sha256_prefix":"ac9ef5c0598455fa","entry":"get_dataset","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"data_loaders/get_data.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/data_loaders/get_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"ac9ef5c0598455fa"}},{"code_sha256_prefix":"66afe7bc37ea03da","entry":"get_dataset_class","repo":"BarqueroGerman/FlowMDM","repo_kind":"official","path":"data_loaders/get_data.py","file_url":"https://github.com/BarqueroGerman/FlowMDM/blob/HEAD/data_loaders/get_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"66afe7bc37ea03da"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}