Browse State-of-the-Art › Human action generation
Human action generation
10 papers with code · 7 benchmarks · 8 datasets archive 2025-07-28
Yan et al. (2019) CSGN:
"When the dancer is stepping, jumping and spinning on the stage, attentions of all audiences are attracted by the streamof the fluent and graceful movements. Building a model that is capable of dancing is as fascinating a task as appreciating the performance itself. In this paper, we aim to generate long-duration human actions represented as skeleton sequences, e.g. those that cover the entirety of a dance, with hundreds of moves and countless possible combinations."
( Image credit: Convolutional Sequence Generation for Skeleton-Based Action Synthesis )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| Human3.6M (5 rows) | Kinetic-GAN | Generative Adversarial Graph Convolutional Networks for Human... | code | — | Compare |
| NTU RGB+D 2D (5 rows) | Kinetic-GAN | Generative Adversarial Graph Convolutional Networks for Human... | code | — | Compare |
| NTU RGB+D (3 rows) | Kinetic-GAN | Generative Adversarial Graph Convolutional Networks for Human... | code | — | Compare |
| NTU RGB+D 120 (2 rows) | Kinetic-GAN | Generative Adversarial Graph Convolutional Networks for Human... | code | — | Compare |
| CMU Mocap (1 row) | ODMO | Action-conditioned On-demand Motion Generation | code | — | Compare |
| HumanAct12 (1 row) | ODMO | Action-conditioned On-demand Motion Generation | code | — | Compare |
| UESTC RGB-D (1 row) | ODMO | Action-conditioned On-demand Motion Generation | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
8 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
10 shown of 10 papers with code (13 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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6 Nov 2014 62 repositories listed Syntology ran 6 of 40 samples · 34 unverified · 6 pointer-only (licence)Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models.
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12 Apr 2021 2 repositories listed Syntology ran 0 of 5 samples · 5 unverifiedBy sampling from this latent space and querying a certain duration through a series of positional encodings, we synthesize variable-length motion sequences conditioned on a categorical action.
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26 May 2018 2 repositories listedInspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions.
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9 Dec 2022 1 repository listedWith the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
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17 Jul 2022 1 repository listedWe propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.
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21 Oct 2021 1 repository listedSynthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise…
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21 Oct 2021 1 repository listedWe introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion.
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30 Jul 2020 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedAction recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
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4 Jul 2020 1 repository listedGenerating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.
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21 Dec 2019 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedIn this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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