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# -*- coding: utf-8 -*-
#
# This file is part of UniRig.
#
# This file is derived from https://github.com/NeuralCarver/Michelangelo
#
# Copyright (c) https://github.com/NeuralCarver/Michelangelo original authors
# Copyright (c) 2025 VAST-AI-Research and contributors.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import torch.nn as nn
from typing import Tuple, List, Optional
import lightning.pytorch as pl
class Point2MeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
self.center = None
self.pc = None
class Latent2MeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
class AlignedMeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
self.surface = None
self.image = None
self.text: Optional[str] = None
self.shape_text_similarity: Optional[float] = None
self.shape_image_similarity: Optional[float] = None
class ShapeAsLatentPLModule(pl.LightningModule):
latent_shape: Tuple[int]
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
class ShapeAsLatentModule(nn.Module):
latent_shape: Tuple[int, int]
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
class AlignedShapeAsLatentPLModule(pl.LightningModule):
latent_shape: Tuple[int]
def set_shape_model_only(self):
raise NotImplementedError
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
class AlignedShapeAsLatentModule(nn.Module):
shape_model: ShapeAsLatentModule
latent_shape: Tuple[int, int]
def __init__(self, *args, **kwargs):
super().__init__()
def set_shape_model_only(self):
raise NotImplementedError
def encode_image_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_text_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_shape_embed(self, *args, **kwargs):
raise NotImplementedError
class TexturedShapeAsLatentModule(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
def query_color(self, *args, **kwargs):
raise NotImplementedError
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