Papers › TEMOS: Generating diverse human motions from textual descriptions
TEMOS: Generating diverse human motions from textual descriptions
Mathis Petrovich, Michael J. Black, Gül Varol
We address the problem of generating diverse 3D human motions from textual descriptions. This challenging task requires joint modeling of both modalities: understanding and extracting useful human-centric information from the text, and then generating plausible and realistic sequences of human poses. In contrast to most previous work which focuses on generating a single, deterministic, motion from a textual description, we design a variational approach that can produce multiple diverse human motions. We propose TEMOS, a text-conditioned generative model leveraging variational autoencoder (VAE) training with human motion data, in combination with a text encoder that produces distribution parameters compatible with the VAE latent space. We show the TEMOS framework can produce both skeleton-based animations as in prior work, as well more expressive SMPL body motions. We evaluate our approach on the KIT Motion-Language benchmark and, despite being relatively straightforward, demonstrate significant improvements over the state of the art. Code and models are available on our webpage.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Motion Synthesis | Inter-X | TEMOS | FID | 29.258 | #5 of 6 | Archive leaderboard | report |
| Motion Synthesis | Inter-X | TEMOS | MMDist | 6.867 | #5 of 6 | Archive leaderboard | report |
| Motion Synthesis | Inter-X | TEMOS | MModality | 0.672 | #5 of 6 | Archive leaderboard | report |
| Motion Synthesis | Inter-X | TEMOS | R-Precision Top3 | 0.238 | #5 of 6 | Archive leaderboard | report |
| Motion Synthesis | InterHuman | TEMOS | FID | 17.375 | #10 of 10 | Archive leaderboard | report |
| Motion Synthesis | InterHuman | TEMOS | MMDist | 6.342 | #10 of 10 | Archive leaderboard | report |
| Motion Synthesis | InterHuman | TEMOS | MModality | 0.535 | #10 of 10 | Archive leaderboard | report |
| Motion Synthesis | InterHuman | TEMOS | R-Precision Top3 | 0.450 | #10 of 10 | 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.
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