TY  - CONF
AU  - German Barquero
AU  - Sergio Escalera
AU  - Cristina Palmero
A2  - ICCV
PY  - 2023//
TI  - BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction
BT  - IEEE/CVF International Conference on Computer Vision (ICCV) Workshops
SP  - 2317
EP  - 2327
N2  - Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behaviorcoupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area ofthe Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
UR  - https://openaccess.thecvf.com/content/ICCV2023/html/Barquero_BeLFusion_Latent_Diffusion_for_Behavior-Driven_Human_Motion_Prediction_ICCV_2023_paper.html
L1  - http://158.109.8.37/files/BEP2022.pdf
N1  - HUPBA; no menciona
ID  - German Barquero2023
ER  -