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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 | |