Datasets:

1Konny
/

B-DoPED

import torch
import numpy as np
import trimesh
import inspect
import os
import sys
if not hasattr(inspect, 'getargspec'):
    inspect.getargspec = inspect.getfullargspec
# chumpy-pickled SMAL models reference removed numpy aliases; restore them.
for name, alias in [('bool', np.bool_), ('int', np.int_), ('float', np.float64), ('complex', np.complex128), ('object', np.object_), ('str', np.str_), ('unicode', str)]:
    if not hasattr(np, name):
        setattr(np, name, alias)

# Locate the BITE checkout (code + SMAL weights). Override with BITE_ROOT env var
# (render_smal_multiview.py --bite-root sets it); default to ./bite_gradio-hf in cwd
# or alongside this file.
def _resolve_bite_root():
    env = os.environ.get('BITE_ROOT')
    if env:
        return os.path.abspath(env)
    here = os.path.dirname(os.path.abspath(__file__))
    for cand in [os.path.join(os.getcwd(), 'bite_gradio-hf'),
                 os.path.join(here, 'bite_gradio-hf'),
                 os.path.join(here, '..', 'bite_gradio-hf')]:
        if os.path.isdir(os.path.join(cand, 'src')):
            return os.path.abspath(cand)
    return os.path.abspath(os.path.join(os.getcwd(), 'bite_gradio-hf'))

BITE_ROOT = _resolve_bite_root()
sys.path.insert(0, os.path.join(BITE_ROOT, 'src'))
from smal_pytorch.smal_model.smal_torch_new import SMAL
from configs.SMAL_configs import SMAL_MODEL_CONFIG
from lifting_to_3d.utils.geometry_utils import rot6d_to_rotmat, rotmat_to_rot6d


def get_optimized_pose_with_glob(orient_6d, pose_6d):
    """Convert 6D rotation representation to rotation matrices for SMAL."""
    batch_size = orient_6d.shape[0]
    orient_rotmat = rot6d_to_rotmat(orient_6d.reshape(-1, 6)).reshape(batch_size, 1, 3, 3)
    pose_rotmat = rot6d_to_rotmat(pose_6d.reshape(-1, 6)).reshape(batch_size, 34, 3, 3)
    return torch.cat([orient_rotmat, pose_rotmat], dim=1)


def initialize_smal_model(
    keyp_conf='red', 
    output_obj_path=None,
    output_npz_path=None,
    output_skeleton_path=None,
    beta=None, 
    betas_limbs=None, 
    pose_6d=None, 
    orient_6d=None, 
    vert_off_compact=None, 
    trans=None, 
    logscale_part_list=None,
    uvmap_image=None,
    xatlas_params=None,
    ):
    """
    Initialize SMAL model in rest pose and save as OBJ file.
    """
    # Set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # SMAL model configuration
    smal_model_type = '39dogs_norm'  # or '39dogs' depending on your needs
    logscale_part_list = SMAL_MODEL_CONFIG[smal_model_type]['logscale_part_list'] if logscale_part_list is None else logscale_part_list
    
    # Initialize SMAL model
    smal = SMAL(
        smal_model_type=smal_model_type, 
        template_name='neutral', 
        logscale_part_list=logscale_part_list
    ).to(device)

    beta_init = torch.zeros(1, 30).to(device)
    # betas_limbs_init = torch.zeros(1, 7).to(device)
    betas_limbs_init = torch.zeros(1, len(logscale_part_list)).to(device)
    identity_3x3 = torch.eye(3, device=device, dtype=torch.float32)
    identity_6d = rotmat_to_rot6d(identity_3x3.unsqueeze(0))
    pose_6d_init = identity_6d.unsqueeze(0).repeat(1, 34, 1)
    orient_6d_init = identity_6d.unsqueeze(0).repeat(1, 1, 1)
    vert_off_compact_init = torch.zeros(1, 2*smal.n_center + 3*smal.n_left).to(device)
    trans_init = torch.zeros(1, 3).to(device)

    if beta is not None:
        beta_init = beta.to(device)
    if betas_limbs is not None:
        betas_limbs_init = betas_limbs.to(device)
    if pose_6d is not None:
        pose_6d = pose_6d.to(device)
    else:
        pose_6d = pose_6d_init
    if orient_6d is not None:
        orient_6d = orient_6d.to(device)
    else:
        orient_6d = orient_6d_init
    if vert_off_compact is not None:
        vert_off_compact_init = vert_off_compact.to(device)
    if trans is not None:
        trans_init = trans.to(device)

    pose_init = get_optimized_pose_with_glob(orient_6d, pose_6d)
    
    with torch.no_grad():
        # Use default parameters for rest pose
        smal_verts, keyp_3d, _ = smal(
            beta=beta_init,  # Shape parameters (neutral)
            betas_limbs=betas_limbs_init,  # Limb parameters (neutral)
            pose=pose_init,  # Rest pose (identity rotations)
            vert_off_compact=vert_off_compact_init,  # No vertex offsets
            trans=trans_init,  # No translation
            keyp_conf=keyp_conf,
            get_skin=True
        )

    # Convert to numpy and create trimesh
    vertices = smal_verts[0].detach().cpu().numpy()
    faces = smal.faces.detach().cpu().numpy()

    # Save mesh and parameters
    mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False, maintain_order=True)

    if xatlas_params is not None:
        mesh.vertices = mesh.vertices[xatlas_params['vmapping']]
        mesh.faces = xatlas_params['faces']
        mesh.visual.uv = xatlas_params['uvs']

    if uvmap_image is not None:
        # apply texture to mesh
        material = trimesh.visual.texture.SimpleMaterial(
            image=uvmap_image, diffuse=(255, 255, 255)
        )
        texture_visuals = trimesh.visual.TextureVisuals(
            uv=mesh.visual.uv, image=uvmap_image, material=material
        )
        mesh.visual = texture_visuals

    if output_obj_path is not None:
        output_obj_path.parent.mkdir(parents=True, exist_ok=True)
        mesh.export(file_obj=output_obj_path)

    if output_npz_path is not None:
        output_npz_path.parent.mkdir(parents=True, exist_ok=True)
        np.savez_compressed(output_npz_path,
            beta=beta_init.data.cpu().numpy(),
            betas_limbs=betas_limbs_init.data.cpu().numpy(),
            pose_6d=pose_6d.data.cpu().numpy(),
            orient_6d=orient_6d.data.cpu().numpy(),
            pose=pose_init.data.cpu().numpy(),
            vert_off_compact=vert_off_compact_init.data.cpu().numpy(),
            trans=trans_init.data.cpu().numpy(),
            keyp_conf=keyp_conf,
        )

    skeleton_mesh = None
    if output_skeleton_path is not None:
        from utils import create_skeleton_visualization  # optional; only for skeleton export
        skeleton_mesh = create_skeleton_visualization(
            keyp_3d.cpu().numpy(),
            smal.parents,
            sphere_radius=0.02,
            line_radius=0.005,
        )
        skeleton_mesh.export(file_obj=output_skeleton_path)

    output = {}
    output['mesh'] = mesh
    output['skeleton_mesh'] = skeleton_mesh
    output['parents'] = smal.parents
    output['vertices'] = vertices
    output['faces'] = faces
    output['keyp_3d'] = keyp_3d
    output['beta'] = beta_init
    output['betas_limbs'] = betas_limbs_init
    output['pose_6d'] = pose_6d
    output['orient_6d'] = orient_6d
    output['vert_off_compact'] = vert_off_compact_init
    output['trans'] = trans_init
    output['keyp_conf'] = keyp_conf
    output['logscale_part_list'] = logscale_part_list
    output['smal_model_type'] = smal_model_type
    output['uvmap_image'] = uvmap_image
    output['xatlas_params'] = xatlas_params
    return output


def initialize_smal_model_batch(
    keyp_conf='red', 
    beta_batch=None,          # (B, 30)
    betas_limbs_batch=None,   # (B, 7) or (B, num_limbs)
    pose_6d_batch=None,       # (B, 34, 6)
    orient_6d_batch=None,     # (B, 1, 6)
    vert_off_compact_batch=None,  # (B, vert_dim)
    trans_batch=None,         # (B, 3)
    logscale_part_list=None,  # shared across batch
    uvmap_image_list=None,    # list of B PIL images or None
    xatlas_params_list=None,  # list of B xatlas param dicts or None
    ):
    """
    Initialize SMAL model in batch mode for faster processing.
    All batch inputs should have the same batch size B.
    Optionally applies texture to each mesh if uvmap_image_list and xatlas_params_list are provided.
    """
    # Set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # SMAL model configuration
    smal_model_type = '39dogs_norm'
    logscale_part_list = SMAL_MODEL_CONFIG[smal_model_type]['logscale_part_list'] if logscale_part_list is None else logscale_part_list
    
    # Initialize SMAL model once
    smal = SMAL(
        smal_model_type=smal_model_type, 
        template_name='neutral', 
        logscale_part_list=logscale_part_list
    ).to(device)

    # Determine batch size from inputs
    batch_size = None
    for tensor in [beta_batch, betas_limbs_batch, pose_6d_batch, orient_6d_batch]:
        if tensor is not None:
            batch_size = tensor.shape[0]
            break
    
    if batch_size is None:
        batch_size = 1

    # Initialize default values
    identity_3x3 = torch.eye(3, device=device, dtype=torch.float32)
    identity_6d = rotmat_to_rot6d(identity_3x3.unsqueeze(0))
    
    # Prepare batch inputs with defaults
    if beta_batch is not None:
        beta_batch = beta_batch.to(device)
    else:
        beta_batch = torch.zeros(batch_size, 30).to(device)
    
    if betas_limbs_batch is not None:
        betas_limbs_batch = betas_limbs_batch.to(device)
    else:
        betas_limbs_batch = torch.zeros(batch_size, len(logscale_part_list)).to(device)
    
    if pose_6d_batch is not None:
        pose_6d_batch = pose_6d_batch.to(device)
    else:
        pose_6d_batch = identity_6d.unsqueeze(0).repeat(batch_size, 34, 1)
    
    if orient_6d_batch is not None:
        orient_6d_batch = orient_6d_batch.to(device)
    else:
        orient_6d_batch = identity_6d.unsqueeze(0).repeat(batch_size, 1, 1)
    
    if vert_off_compact_batch is not None:
        vert_off_compact_batch = vert_off_compact_batch.to(device)
    else:
        vert_off_compact_batch = torch.zeros(batch_size, 2*smal.n_center + 3*smal.n_left).to(device)
    
    if trans_batch is not None:
        trans_batch = trans_batch.to(device)
    else:
        trans_batch = torch.zeros(batch_size, 3).to(device)

    # Convert 6D rotations to rotation matrices for the entire batch
    # orient_6d_batch: (B, 1, 6) -> (B, 1, 3, 3)
    # pose_6d_batch: (B, 34, 6) -> (B, 34, 3, 3)
    orient_rotmat = rot6d_to_rotmat(orient_6d_batch.reshape(-1, 6)).reshape(batch_size, 1, 3, 3)
    pose_rotmat = rot6d_to_rotmat(pose_6d_batch.reshape(-1, 6)).reshape(batch_size, 34, 3, 3)
    pose_batch = torch.cat([orient_rotmat, pose_rotmat], dim=1)  # (B, 35, 3, 3)
    
    with torch.no_grad():
        # Batch forward pass
        smal_verts, keyp_3d, _ = smal(
            beta=beta_batch,
            betas_limbs=betas_limbs_batch,
            pose=pose_batch,
            vert_off_compact=vert_off_compact_batch,
            trans=trans_batch,
            keyp_conf=keyp_conf,
            get_skin=True
        )

    # Convert to numpy and create trimesh objects for each sample
    vertices_batch = smal_verts.detach().cpu().numpy()  # (B, V, 3)
    faces = smal.faces.detach().cpu().numpy()
    keyp_3d_batch = keyp_3d.detach().cpu()  # (B, J, 3)
    
    # Create output list
    outputs = []
    for i in range(batch_size):
        vertices = vertices_batch[i]
        mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False, maintain_order=True)
        
        # Get texture params for this sample
        xatlas_params = xatlas_params_list[i] if xatlas_params_list is not None else None
        uvmap_image = uvmap_image_list[i] if uvmap_image_list is not None else None
        
        # Apply xatlas remapping if available
        if xatlas_params is not None:
            mesh.vertices = mesh.vertices[xatlas_params['vmapping']]
            mesh.faces = xatlas_params['faces']
            mesh.visual.uv = xatlas_params['uvs']
        
        # Apply texture if available
        if uvmap_image is not None:
            material = trimesh.visual.texture.SimpleMaterial(
                image=uvmap_image, diffuse=(255, 255, 255)
            )
            texture_visuals = trimesh.visual.TextureVisuals(
                uv=mesh.visual.uv, image=uvmap_image, material=material
            )
            mesh.visual = texture_visuals
        
        output = {}
        output['mesh'] = mesh
        output['skeleton_mesh'] = None
        output['parents'] = smal.parents
        output['vertices'] = vertices
        output['faces'] = faces
        output['keyp_3d'] = keyp_3d_batch[i:i+1]  # Keep as (1, J, 3) for compatibility
        output['beta'] = beta_batch[i:i+1]
        output['betas_limbs'] = betas_limbs_batch[i:i+1]
        output['pose_6d'] = pose_6d_batch[i:i+1]
        output['orient_6d'] = orient_6d_batch[i:i+1]
        output['vert_off_compact'] = vert_off_compact_batch[i:i+1]
        output['trans'] = trans_batch[i:i+1]
        output['keyp_conf'] = keyp_conf
        output['logscale_part_list'] = logscale_part_list
        output['smal_model_type'] = smal_model_type
        output['uvmap_image'] = uvmap_image
        output['xatlas_params'] = xatlas_params
        outputs.append(output)
    
    return outputs

	import torch
	import numpy as np
	import trimesh
	import inspect
	import os
	import sys
	if not hasattr(inspect, 'getargspec'):
	inspect.getargspec = inspect.getfullargspec
	# chumpy-pickled SMAL models reference removed numpy aliases; restore them.
	for name, alias in [('bool', np.bool_), ('int', np.int_), ('float', np.float64), ('complex', np.complex128), ('object', np.object_), ('str', np.str_), ('unicode', str)]:
	if not hasattr(np, name):
	setattr(np, name, alias)

	# Locate the BITE checkout (code + SMAL weights). Override with BITE_ROOT env var
	# (render_smal_multiview.py --bite-root sets it); default to ./bite_gradio-hf in cwd
	# or alongside this file.
	def _resolve_bite_root():
	env = os.environ.get('BITE_ROOT')
	if env:
	return os.path.abspath(env)
	here = os.path.dirname(os.path.abspath(__file__))
	for cand in [os.path.join(os.getcwd(), 'bite_gradio-hf'),
	os.path.join(here, 'bite_gradio-hf'),
	os.path.join(here, '..', 'bite_gradio-hf')]:
	if os.path.isdir(os.path.join(cand, 'src')):
	return os.path.abspath(cand)
	return os.path.abspath(os.path.join(os.getcwd(), 'bite_gradio-hf'))

	BITE_ROOT = _resolve_bite_root()
	sys.path.insert(0, os.path.join(BITE_ROOT, 'src'))
	from smal_pytorch.smal_model.smal_torch_new import SMAL
	from configs.SMAL_configs import SMAL_MODEL_CONFIG
	from lifting_to_3d.utils.geometry_utils import rot6d_to_rotmat, rotmat_to_rot6d


	def get_optimized_pose_with_glob(orient_6d, pose_6d):
	"""Convert 6D rotation representation to rotation matrices for SMAL."""
	batch_size = orient_6d.shape[0]
	orient_rotmat = rot6d_to_rotmat(orient_6d.reshape(-1, 6)).reshape(batch_size, 1, 3, 3)
	pose_rotmat = rot6d_to_rotmat(pose_6d.reshape(-1, 6)).reshape(batch_size, 34, 3, 3)
	return torch.cat([orient_rotmat, pose_rotmat], dim=1)


	def initialize_smal_model(
	keyp_conf='red',
	output_obj_path=None,
	output_npz_path=None,
	output_skeleton_path=None,
	beta=None,
	betas_limbs=None,
	pose_6d=None,
	orient_6d=None,
	vert_off_compact=None,
	trans=None,
	logscale_part_list=None,
	uvmap_image=None,
	xatlas_params=None,
	):
	"""
	Initialize SMAL model in rest pose and save as OBJ file.
	"""
	# Set device
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

	# SMAL model configuration
	smal_model_type = '39dogs_norm' # or '39dogs' depending on your needs
	logscale_part_list = SMAL_MODEL_CONFIG[smal_model_type]['logscale_part_list'] if logscale_part_list is None else logscale_part_list

	# Initialize SMAL model
	smal = SMAL(
	smal_model_type=smal_model_type,
	template_name='neutral',
	logscale_part_list=logscale_part_list
	).to(device)

	beta_init = torch.zeros(1, 30).to(device)
	# betas_limbs_init = torch.zeros(1, 7).to(device)
	betas_limbs_init = torch.zeros(1, len(logscale_part_list)).to(device)
	identity_3x3 = torch.eye(3, device=device, dtype=torch.float32)
	identity_6d = rotmat_to_rot6d(identity_3x3.unsqueeze(0))
	pose_6d_init = identity_6d.unsqueeze(0).repeat(1, 34, 1)
	orient_6d_init = identity_6d.unsqueeze(0).repeat(1, 1, 1)
	vert_off_compact_init = torch.zeros(1, 2smal.n_center + 3smal.n_left).to(device)
	trans_init = torch.zeros(1, 3).to(device)

	if beta is not None:
	beta_init = beta.to(device)
	if betas_limbs is not None:
	betas_limbs_init = betas_limbs.to(device)
	if pose_6d is not None:
	pose_6d = pose_6d.to(device)
	else:
	pose_6d = pose_6d_init
	if orient_6d is not None:
	orient_6d = orient_6d.to(device)
	else:
	orient_6d = orient_6d_init
	if vert_off_compact is not None:
	vert_off_compact_init = vert_off_compact.to(device)
	if trans is not None:
	trans_init = trans.to(device)

	pose_init = get_optimized_pose_with_glob(orient_6d, pose_6d)

	with torch.no_grad():
	# Use default parameters for rest pose
	smal_verts, keyp_3d, _ = smal(
	beta=beta_init, # Shape parameters (neutral)
	betas_limbs=betas_limbs_init, # Limb parameters (neutral)
	pose=pose_init, # Rest pose (identity rotations)
	vert_off_compact=vert_off_compact_init, # No vertex offsets
	trans=trans_init, # No translation
	keyp_conf=keyp_conf,
	get_skin=True
	)

	# Convert to numpy and create trimesh
	vertices = smal_verts[0].detach().cpu().numpy()
	faces = smal.faces.detach().cpu().numpy()

	# Save mesh and parameters
	mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False, maintain_order=True)

	if xatlas_params is not None:
	mesh.vertices = mesh.vertices[xatlas_params['vmapping']]
	mesh.faces = xatlas_params['faces']
	mesh.visual.uv = xatlas_params['uvs']

	if uvmap_image is not None:
	# apply texture to mesh
	material = trimesh.visual.texture.SimpleMaterial(
	image=uvmap_image, diffuse=(255, 255, 255)
	)
	texture_visuals = trimesh.visual.TextureVisuals(
	uv=mesh.visual.uv, image=uvmap_image, material=material
	)
	mesh.visual = texture_visuals

	if output_obj_path is not None:
	output_obj_path.parent.mkdir(parents=True, exist_ok=True)
	mesh.export(file_obj=output_obj_path)

	if output_npz_path is not None:
	output_npz_path.parent.mkdir(parents=True, exist_ok=True)
	np.savez_compressed(output_npz_path,
	beta=beta_init.data.cpu().numpy(),
	betas_limbs=betas_limbs_init.data.cpu().numpy(),
	pose_6d=pose_6d.data.cpu().numpy(),
	orient_6d=orient_6d.data.cpu().numpy(),
	pose=pose_init.data.cpu().numpy(),
	vert_off_compact=vert_off_compact_init.data.cpu().numpy(),
	trans=trans_init.data.cpu().numpy(),
	keyp_conf=keyp_conf,
	)

	skeleton_mesh = None
	if output_skeleton_path is not None:
	from utils import create_skeleton_visualization # optional; only for skeleton export
	skeleton_mesh = create_skeleton_visualization(
	keyp_3d.cpu().numpy(),
	smal.parents,
	sphere_radius=0.02,
	line_radius=0.005,
	)
	skeleton_mesh.export(file_obj=output_skeleton_path)

	output = {}
	output['mesh'] = mesh
	output['skeleton_mesh'] = skeleton_mesh
	output['parents'] = smal.parents
	output['vertices'] = vertices
	output['faces'] = faces
	output['keyp_3d'] = keyp_3d
	output['beta'] = beta_init
	output['betas_limbs'] = betas_limbs_init
	output['pose_6d'] = pose_6d
	output['orient_6d'] = orient_6d
	output['vert_off_compact'] = vert_off_compact_init
	output['trans'] = trans_init
	output['keyp_conf'] = keyp_conf
	output['logscale_part_list'] = logscale_part_list
	output['smal_model_type'] = smal_model_type
	output['uvmap_image'] = uvmap_image
	output['xatlas_params'] = xatlas_params
	return output


	def initialize_smal_model_batch(
	keyp_conf='red',
	beta_batch=None, # (B, 30)
	betas_limbs_batch=None, # (B, 7) or (B, num_limbs)
	pose_6d_batch=None, # (B, 34, 6)
	orient_6d_batch=None, # (B, 1, 6)
	vert_off_compact_batch=None, # (B, vert_dim)
	trans_batch=None, # (B, 3)
	logscale_part_list=None, # shared across batch
	uvmap_image_list=None, # list of B PIL images or None
	xatlas_params_list=None, # list of B xatlas param dicts or None
	):
	"""
	Initialize SMAL model in batch mode for faster processing.
	All batch inputs should have the same batch size B.
	Optionally applies texture to each mesh if uvmap_image_list and xatlas_params_list are provided.
	"""
	# Set device
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

	# SMAL model configuration
	smal_model_type = '39dogs_norm'
	logscale_part_list = SMAL_MODEL_CONFIG[smal_model_type]['logscale_part_list'] if logscale_part_list is None else logscale_part_list

	# Initialize SMAL model once
	smal = SMAL(
	smal_model_type=smal_model_type,
	template_name='neutral',
	logscale_part_list=logscale_part_list
	).to(device)

	# Determine batch size from inputs
	batch_size = None
	for tensor in [beta_batch, betas_limbs_batch, pose_6d_batch, orient_6d_batch]:
	if tensor is not None:
	batch_size = tensor.shape[0]
	break

	if batch_size is None:
	batch_size = 1

	# Initialize default values
	identity_3x3 = torch.eye(3, device=device, dtype=torch.float32)
	identity_6d = rotmat_to_rot6d(identity_3x3.unsqueeze(0))

	# Prepare batch inputs with defaults
	if beta_batch is not None:
	beta_batch = beta_batch.to(device)
	else:
	beta_batch = torch.zeros(batch_size, 30).to(device)

	if betas_limbs_batch is not None:
	betas_limbs_batch = betas_limbs_batch.to(device)
	else:
	betas_limbs_batch = torch.zeros(batch_size, len(logscale_part_list)).to(device)

	if pose_6d_batch is not None:
	pose_6d_batch = pose_6d_batch.to(device)
	else:
	pose_6d_batch = identity_6d.unsqueeze(0).repeat(batch_size, 34, 1)

	if orient_6d_batch is not None:
	orient_6d_batch = orient_6d_batch.to(device)
	else:
	orient_6d_batch = identity_6d.unsqueeze(0).repeat(batch_size, 1, 1)

	if vert_off_compact_batch is not None:
	vert_off_compact_batch = vert_off_compact_batch.to(device)
	else:
	vert_off_compact_batch = torch.zeros(batch_size, 2smal.n_center + 3smal.n_left).to(device)

	if trans_batch is not None:
	trans_batch = trans_batch.to(device)
	else:
	trans_batch = torch.zeros(batch_size, 3).to(device)

	# Convert 6D rotations to rotation matrices for the entire batch
	# orient_6d_batch: (B, 1, 6) -> (B, 1, 3, 3)
	# pose_6d_batch: (B, 34, 6) -> (B, 34, 3, 3)
	orient_rotmat = rot6d_to_rotmat(orient_6d_batch.reshape(-1, 6)).reshape(batch_size, 1, 3, 3)
	pose_rotmat = rot6d_to_rotmat(pose_6d_batch.reshape(-1, 6)).reshape(batch_size, 34, 3, 3)
	pose_batch = torch.cat([orient_rotmat, pose_rotmat], dim=1) # (B, 35, 3, 3)

	with torch.no_grad():
	# Batch forward pass
	smal_verts, keyp_3d, _ = smal(
	beta=beta_batch,
	betas_limbs=betas_limbs_batch,
	pose=pose_batch,
	vert_off_compact=vert_off_compact_batch,
	trans=trans_batch,
	keyp_conf=keyp_conf,
	get_skin=True
	)

	# Convert to numpy and create trimesh objects for each sample
	vertices_batch = smal_verts.detach().cpu().numpy() # (B, V, 3)
	faces = smal.faces.detach().cpu().numpy()
	keyp_3d_batch = keyp_3d.detach().cpu() # (B, J, 3)

	# Create output list
	outputs = []
	for i in range(batch_size):
	vertices = vertices_batch[i]
	mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False, maintain_order=True)

	# Get texture params for this sample
	xatlas_params = xatlas_params_list[i] if xatlas_params_list is not None else None
	uvmap_image = uvmap_image_list[i] if uvmap_image_list is not None else None

	# Apply xatlas remapping if available
	if xatlas_params is not None:
	mesh.vertices = mesh.vertices[xatlas_params['vmapping']]
	mesh.faces = xatlas_params['faces']
	mesh.visual.uv = xatlas_params['uvs']

	# Apply texture if available
	if uvmap_image is not None:
	material = trimesh.visual.texture.SimpleMaterial(
	image=uvmap_image, diffuse=(255, 255, 255)
	)
	texture_visuals = trimesh.visual.TextureVisuals(
	uv=mesh.visual.uv, image=uvmap_image, material=material
	)
	mesh.visual = texture_visuals

	output = {}
	output['mesh'] = mesh
	output['skeleton_mesh'] = None
	output['parents'] = smal.parents
	output['vertices'] = vertices
	output['faces'] = faces
	output['keyp_3d'] = keyp_3d_batch[i:i+1] # Keep as (1, J, 3) for compatibility
	output['beta'] = beta_batch[i:i+1]
	output['betas_limbs'] = betas_limbs_batch[i:i+1]
	output['pose_6d'] = pose_6d_batch[i:i+1]
	output['orient_6d'] = orient_6d_batch[i:i+1]
	output['vert_off_compact'] = vert_off_compact_batch[i:i+1]
	output['trans'] = trans_batch[i:i+1]
	output['keyp_conf'] = keyp_conf
	output['logscale_part_list'] = logscale_part_list
	output['smal_model_type'] = smal_model_type
	output['uvmap_image'] = uvmap_image
	output['xatlas_params'] = xatlas_params
	outputs.append(output)

	return outputs