Function compute_foot_sliding

mbench/motion_quality.py:362–404

Check for foot sliding in the motion data.

(full_info_path: str, device: str, **kwargs)

    }

def compute_foot_sliding(full_info_path: str, device: str, **kwargs):
    """
    Check for foot sliding in the motion data.
    """
    prompt_dict_ls = load_dimension_info(full_info_path, dimension='Foot_Sliding')
    
    sliding_list = []
    per_motion_metrics = []
    
    for prompt_dict in tqdm(prompt_dict_ls):
        evaluation_file = prompt_dict["evaluation_file"]
        pred_joints = load_joints(evaluation_file, device)

        
        contact = get_contact(pred_joints, device=device)
        
        foot_pos = pred_joints[:, FOOT_IDX]   # (frames, 2, 3)
        # Compute foot velocity and delta joints
        foot_vel = foot_pos[1:] - foot_pos[:-1]
        foot_vel = torch.cat([foot_vel, foot_vel[-1:]], dim=0)
        foot_delta = torch.norm(foot_vel[:, :, :2], dim=-1)
        
        # Calculate sliding distance for left and right foot
        left_sliding_dis = (foot_delta[:, 0] * contact[:, 0]).sum(dim=0) / ((contact[:, 0]).sum(dim=0) + 1e-6)
        right_sliding_dis = (foot_delta[:, 1] * contact[:, 1]).sum(dim=0) / ((contact[:, 1]).sum(dim=0) + 1e-6)

        sliding_score = (left_sliding_dis + right_sliding_dis) / 2
        sliding_value = sliding_score.item()
        sliding_list.append(sliding_value)
        per_motion_metrics.append(
            {
                "id": prompt_dict.get("id"),
                "prompt": prompt_dict.get("prompt"),
                "value": sliding_value,
                "evaluation_file": evaluation_file,
                "motion_duration": prompt_dict.get("motion_duration"),
            }
        )
    
    return {
        "aggregate": summarize_scores(sliding_list),
        "per_motion": per_motion_metrics,
    }

def compute_dynamic_degree(full_info_path: str, device: str, **kwargs):
    """