[ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction".

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

[ECCV 2020] Official PyTorch Implementation of "DLow: Diversifying Latent Flows for Diverse Human Motion Prediction". ECCV 2020.

[ICCV2023] Official PyTorch Implementation of "BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction". ICCV 2023

Code and notebook for our paper "Assessing interaction recovery of predicted protein-ligand poses"

Comprehensive ensembling of protein-ligand structure and affinity prediction methods (CASP16)

A Set of Machine Learning Classifiers For RNA Containing Complexes

This project predicts future human body poses from past pose sequences using the SMPL model encoded via VPoser. We model the task as a time-series prediction in VPoser latent space using a bidirectional GRU encoder-decoder.

Fine-tuning OpenFold3 (4.0.0) for PDE10A protein–ligand pose prediction via distribution-aware PDB-scale data augmentation: +0.20 PL LDDT, −2.3 Å ligand RMSD on held-out.