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LICENSE_NVIDIA

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+Copyright (c) 2021-2022, NVIDIA Corporation & affiliates. All rights
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+     possible, most closely approximates an absolute disclaimer and
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+Section 6 -- Term and Termination.
+
+  a. This Public License applies for the term of the Copyright and
+     Similar Rights licensed here. However, if You fail to comply with
+     this Public License, then Your rights under this Public License
+     terminate automatically.
+
+  b. Where Your right to use the Licensed Material has terminated under
+     Section 6(a), it reinstates:
+
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+          it is cured within 30 days of Your discovery of the
+          violation; or
+
+       2. upon express reinstatement by the Licensor.
+
+     For the avoidance of doubt, this Section 6(b) does not affect any
+     right the Licensor may have to seek remedies for Your violations
+     of this Public License.
+
+  c. For the avoidance of doubt, the Licensor may also offer the
+     Licensed Material under separate terms or conditions or stop
+     distributing the Licensed Material at any time; however, doing so
+     will not terminate this Public License.
+
+  d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
+     License.
+
+
+Section 7 -- Other Terms and Conditions.
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+  b. Any arrangements, understandings, or agreements regarding the
+     Licensed Material not stated herein are separate from and
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+
+Section 8 -- Interpretation.
+
+  a. For the avoidance of doubt, this Public License does not, and
+     shall not be interpreted to, reduce, limit, restrict, or impose
+     conditions on any use of the Licensed Material that could lawfully
+     be made without permission under this Public License.
+
+  b. To the extent possible, if any provision of this Public License is
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+     minimum extent necessary to make it enforceable. If the provision
+     cannot be reformed, it shall be severed from this Public License
+     without affecting the enforceability of the remaining terms and
+     conditions.
+
+  c. No term or condition of this Public License will be waived and no
+     failure to comply consented to unless expressly agreed to by the
+     Licensor.
+
+  d. Nothing in this Public License constitutes or may be interpreted
+     as a limitation upon, or waiver of, any privileges and immunities
+     that apply to the Licensor or You, including from the legal
+     processes of any jurisdiction or authority.
+
+=======================================================================
+
+Creative Commons is not a party to its public
+licenses. Notwithstanding, Creative Commons may elect to apply one of
+its public licenses to material it publishes and in those instances
+will be considered the “Licensor.” The text of the Creative Commons
+public licenses is dedicated to the public domain under the CC0 Public
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lrm/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Empty

lrm/cam_utils.py

@@ -0,0 +1,128 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+
+"""
+R: (N, 3, 3)
+T: (N, 3)
+E: (N, 4, 4)
+vector: (N, 3)
+"""
+
+
+def compose_extrinsic_R_T(R: torch.Tensor, T: torch.Tensor):
+    """
+    Compose the standard form extrinsic matrix from R and T.
+    Batched I/O.
+    """
+    RT = torch.cat((R, T.unsqueeze(-1)), dim=-1)
+    return compose_extrinsic_RT(RT)
+
+
+def compose_extrinsic_RT(RT: torch.Tensor):
+    """
+    Compose the standard form extrinsic matrix from RT.
+    Batched I/O.
+    """
+    return torch.cat([
+        RT,
+        torch.tensor([[[0, 0, 0, 1]]], dtype=torch.float32).repeat(RT.shape[0], 1, 1)
+        ], dim=1)
+
+
+def decompose_extrinsic_R_T(E: torch.Tensor):
+    """
+    Decompose the standard extrinsic matrix into R and T.
+    Batched I/O.
+    """
+    RT = decompose_extrinsic_RT(E)
+    return RT[:, :, :3], RT[:, :, 3]
+
+
+def decompose_extrinsic_RT(E: torch.Tensor):
+    """
+    Decompose the standard extrinsic matrix into RT.
+    Batched I/O.
+    """
+    return E[:, :3, :]
+
+
+def get_normalized_camera_intrinsics(intrinsics: torch.Tensor):
+    """
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    Return batched fx, fy, cx, cy
+    """
+    fx, fy = intrinsics[:, 0, 0], intrinsics[:, 0, 1]
+    cx, cy = intrinsics[:, 1, 0], intrinsics[:, 1, 1]
+    width, height = intrinsics[:, 2, 0], intrinsics[:, 2, 1]
+    fx, fy = fx / width, fy / height
+    cx, cy = cx / width, cy / height
+    return fx, fy, cx, cy
+
+
+def build_camera_principle(RT: torch.Tensor, intrinsics: torch.Tensor):
+    """
+    RT: (N, 3, 4)
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    """
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    return torch.cat([
+        RT.reshape(-1, 12),
+        fx.unsqueeze(-1), fy.unsqueeze(-1), cx.unsqueeze(-1), cy.unsqueeze(-1),
+    ], dim=-1)
+
+
+def build_camera_standard(RT: torch.Tensor, intrinsics: torch.Tensor):
+    """
+    RT: (N, 3, 4)
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    """
+    E = compose_extrinsic_RT(RT)
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    I = torch.stack([
+        torch.stack([fx, torch.zeros_like(fx), cx], dim=-1),
+        torch.stack([torch.zeros_like(fy), fy, cy], dim=-1),
+        torch.tensor([[0, 0, 1]], dtype=torch.float32, device=RT.device).repeat(RT.shape[0], 1),
+    ], dim=1)
+    return torch.cat([
+        E.reshape(-1, 16),
+        I.reshape(-1, 9),
+    ], dim=-1)
+
+
+def center_looking_at_camera_pose(camera_position: torch.Tensor, look_at: torch.Tensor = None, up_world: torch.Tensor = None):
+    """
+    camera_position: (M, 3)
+    look_at: (3)
+    up_world: (3)
+    return: (M, 3, 4)
+    """
+    # by default, looking at the origin and world up is pos-z
+    if look_at is None:
+        look_at = torch.tensor([0, 0, 0], dtype=torch.float32)
+    if up_world is None:
+        up_world = torch.tensor([0, 0, 1], dtype=torch.float32)
+    look_at = look_at.unsqueeze(0).repeat(camera_position.shape[0], 1)
+    up_world = up_world.unsqueeze(0).repeat(camera_position.shape[0], 1)
+
+    z_axis = camera_position - look_at
+    z_axis = z_axis / z_axis.norm(dim=-1, keepdim=True)
+    x_axis = torch.cross(up_world, z_axis)
+    x_axis = x_axis / x_axis.norm(dim=-1, keepdim=True)
+    y_axis = torch.cross(z_axis, x_axis)
+    y_axis = y_axis / y_axis.norm(dim=-1, keepdim=True)
+    extrinsics = torch.stack([x_axis, y_axis, z_axis, camera_position], dim=-1)
+    return extrinsics

lrm/inferrer.py

@@ -0,0 +1,260 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+import math
+import os
+import imageio
+import mcubes
+import trimesh
+import numpy as np
+import argparse
+from PIL import Image
+
+from .models.generator import LRMGenerator
+from .cam_utils import build_camera_principle, build_camera_standard, center_looking_at_camera_pose
+
+
+class LRMInferrer:
+    def __init__(self, model_name: str):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+        _checkpoint = self._load_checkpoint(model_name)
+        _model_weights, _model_kwargs = _checkpoint['weights'], _checkpoint['kwargs']['model']
+        self.model = self._build_model(_model_kwargs, _model_weights).eval()
+
+        self.infer_kwargs = _checkpoint['kwargs']['infer']
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        pass
+
+    def _load_checkpoint(self, model_name: str, cache_dir = './.cache'):
+        # download checkpoint if not exists
+        if not os.path.exists(cache_dir):
+            os.makedirs(cache_dir, exist_ok=True)
+        if not os.path.exists(os.path.join(cache_dir, f'{model_name}.pth')):
+            # TODO: on-the-fly download not supported yet, plz download manually
+            # os.system(f'wget -O {os.path.join(cache_dir, f"{model_name}.pth")} https://zxhezexin.com/modelzoo/openlrm/{model_name}.pth')
+            raise FileNotFoundError(f"Checkpoint {model_name} not found in {cache_dir}")
+        local_path = os.path.join(cache_dir, f'{model_name}.pth')
+        checkpoint = torch.load(local_path, map_location=self.device)
+        return checkpoint
+
+    def _build_model(self, model_kwargs, model_weights):
+        model = LRMGenerator(**model_kwargs).to(self.device)
+        model.load_state_dict(model_weights)
+        print(f"======== Loaded model from checkpoint ========")
+        return model
+
+    @staticmethod
+    def _get_surrounding_views(M: int = 160, radius: float = 2.0, height: float = 0.8):
+        # M: number of surrounding views
+        # radius: camera dist to center
+        # height: height of the camera
+        # return: (M, 3, 4)
+        assert M > 0
+        assert radius > 0
+
+        camera_positions = []
+        projected_radius = math.sqrt(radius ** 2 - height ** 2)
+        for i in range(M):
+            theta = 2 * math.pi * i / M - math.pi / 2
+            x = projected_radius * math.cos(theta)
+            y = projected_radius * math.sin(theta)
+            z = height
+            camera_positions.append([x, y, z])
+        camera_positions = torch.tensor(camera_positions, dtype=torch.float32)
+        extrinsics = center_looking_at_camera_pose(camera_positions)
+
+        return extrinsics
+
+    @staticmethod
+    def _default_intrinsics():
+        # return: (3, 2)
+        fx = fy = 384
+        cx = cy = 256
+        w = h = 512
+        intrinsics = torch.tensor([
+            [fx, fy],
+            [cx, cy],
+            [w, h],
+        ], dtype=torch.float32)
+        return intrinsics
+
+    def _default_source_camera(self, batch_size: int = 1):
+        # return: (N, D_cam_raw)
+        dist_to_center = 2
+        canonical_camera_extrinsics = torch.tensor([[
+            [1, 0, 0, 0],
+            [0, 0, -1, -dist_to_center],
+            [0, 1, 0, 0],
+        ]], dtype=torch.float32)
+        canonical_camera_intrinsics = self._default_intrinsics().unsqueeze(0)
+        source_camera = build_camera_principle(canonical_camera_extrinsics, canonical_camera_intrinsics)
+        return source_camera.repeat(batch_size, 1)
+
+    def _default_render_cameras(self, batch_size: int = 1):
+        # return: (N, M, D_cam_render)
+        render_camera_extrinsics = self._get_surrounding_views()
+        render_camera_intrinsics = self._default_intrinsics().unsqueeze(0).repeat(render_camera_extrinsics.shape[0], 1, 1)
+        render_cameras = build_camera_standard(render_camera_extrinsics, render_camera_intrinsics)
+        return render_cameras.unsqueeze(0).repeat(batch_size, 1, 1)
+
+    @staticmethod
+    def images_to_video(images, output_path, fps, verbose=False):
+        # images: (T, C, H, W)
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        frames = []
+        for i in range(images.shape[0]):
+            frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
+            assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
+                f"Frame shape mismatch: {frame.shape} vs {images.shape}"
+            assert frame.min() >= 0 and frame.max() <= 255, \
+                f"Frame value out of range: {frame.min()} ~ {frame.max()}"
+            frames.append(frame)
+        imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='mpeg4', quality=10)
+        if verbose:
+            print(f"Saved video to {output_path}")
+
+    def infer_single(self, image: torch.Tensor, render_size: int, mesh_size: int, export_video: bool, export_mesh: bool):
+        # image: [1, C_img, H_img, W_img]
+        mesh_thres = 1.0
+        chunk_size = 2
+        batch_size = 1
+
+        source_camera = self._default_source_camera(batch_size).to(self.device)
+        render_cameras = self._default_render_cameras(batch_size).to(self.device)
+
+        with torch.no_grad():
+            planes = self.model.forward_planes(image, source_camera)
+            results = {}
+
+            if export_video:
+                # forward synthesizer per mini-batch
+                frames = []
+                for i in range(0, render_cameras.shape[1], chunk_size):
+                    frames.append(
+                        self.model.synthesizer(
+                            planes,
+                            render_cameras[:, i:i+chunk_size],
+                            render_size,
+                        )
+                    )
+                # merge frames
+                frames = {
+                    k: torch.cat([r[k] for r in frames], dim=1)
+                    for k in frames[0].keys()
+                }
+                # update results
+                results.update({
+                    'frames': frames,
+                })
+
+            if export_mesh:
+                grid_out = self.model.synthesizer.forward_grid(
+                    planes=planes,
+                    grid_size=mesh_size,
+                )
+                
+                vtx, faces = mcubes.marching_cubes(grid_out['sigma'].squeeze(0).squeeze(-1).cpu().numpy(), mesh_thres)
+                vtx = vtx / (mesh_size - 1) * 2 - 1
+
+                vtx_tensor = torch.tensor(vtx, dtype=torch.float32, device=self.device).unsqueeze(0)
+                vtx_colors = self.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].squeeze(0).cpu().numpy()  # (0, 1)
+                vtx_colors = (vtx_colors * 255).astype(np.uint8)
+                
+                mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+
+                results.update({
+                    'mesh': mesh,
+                })
+
+            return results
+
+    def infer(self, source_image: str, dump_path: str, source_size: int, render_size: int, mesh_size: int, export_video: bool, export_mesh: bool):
+
+        source_image_size = source_size if source_size > 0 else self.infer_kwargs['source_size']
+
+        image = torch.tensor(np.array(Image.open(source_image))).permute(2, 0, 1).unsqueeze(0) / 255.0
+        # if RGBA, blend to RGB
+        if image.shape[1] == 4:
+            image = image[:, :3, ...] * image[:, 3:, ...] + (1 - image[:, 3:, ...])
+        image = torch.nn.functional.interpolate(image, size=(source_image_size, source_image_size), mode='bicubic', align_corners=True)
+        image = torch.clamp(image, 0, 1)
+        results = self.infer_single(
+            image.cuda(),
+            render_size=render_size if render_size > 0 else self.infer_kwargs['render_size'],
+            mesh_size=mesh_size,
+            export_video=export_video,
+            export_mesh=export_mesh,
+        )
+
+        image_name = os.path.basename(source_image)
+        uid = image_name.split('.')[0]
+
+        os.makedirs(dump_path, exist_ok=True)
+
+        # dump video
+        if 'frames' in results:
+            renderings = results['frames']
+            for k, v in renderings.items():
+                if k == 'images_rgb':
+                    self.images_to_video(
+                        v[0],
+                        os.path.join(dump_path, f'{uid}.mov'),
+                        fps=40,
+                    )
+                else:
+                    # torch.save(v[0], os.path.join(dump_path, f'{uid}_{k}.pth'))
+                    pass
+
+        # dump mesh
+        if 'mesh' in results:
+            mesh = results['mesh']
+            # save ply format mesh
+            mesh.export(os.path.join(dump_path, f'{uid}.ply'), 'ply')
+
+
+if __name__ == '__main__':
+
+    """
+    Example usage:
+    python -m lrm.inferrer --model_name lrm-base-obj-v1 --source_image ./assets/sample_input/owl.png --export_video --export_mesh
+    """
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name', type=str, default='lrm-base-obj-v1')
+    parser.add_argument('--source_image', type=str, default='./assets/sample_input/owl.png')
+    parser.add_argument('--dump_path', type=str, default='./dumps')
+    parser.add_argument('--source_size', type=int, default=-1)
+    parser.add_argument('--render_size', type=int, default=-1)
+    parser.add_argument('--mesh_size', type=int, default=384)
+    parser.add_argument('--export_video', action='store_true')
+    parser.add_argument('--export_mesh', action='store_true')
+    args = parser.parse_args()
+
+    with LRMInferrer(model_name=args.model_name) as inferrer:
+        inferrer.infer(
+            source_image=args.source_image,
+            dump_path=args.dump_path,
+            source_size=args.source_size,
+            render_size=args.render_size,
+            mesh_size=args.mesh_size,
+            export_video=args.export_video,
+            export_mesh=args.export_mesh,
+        )

lrm/models/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Empty

lrm/models/encoders/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Empty

lrm/models/encoders/dino_wrapper.py

@@ -0,0 +1,59 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch.nn as nn
+from transformers import ViTImageProcessor, ViTModel
+
+
+class DinoWrapper(nn.Module):
+    """
+    Dino v1 wrapper using huggingface transformer implementation.
+    """
+    def __init__(self, model_name: str, freeze: bool = True):
+        super().__init__()
+        self.model, self.processor = self._build_dino(model_name)
+        if freeze:
+            self._freeze()
+
+    def forward(self, image):
+        # image: [N, C, H, W], on cpu
+        # RGB image with [0,1] scale and properly sized
+        inputs = self.processor(images=image, return_tensors="pt", do_rescale=False, do_resize=False).to(self.model.device)
+        # This resampling of positional embedding uses bicubic interpolation
+        outputs = self.model(**inputs, interpolate_pos_encoding=True)
+        last_hidden_states = outputs.last_hidden_state
+        return last_hidden_states
+
+    def _freeze(self):
+        print(f"======== Freezing DinoWrapper ========")
+        self.model.eval()
+        for name, param in self.model.named_parameters():
+            param.requires_grad = False
+
+    @staticmethod
+    def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5):
+        import requests
+        try:
+            model = ViTModel.from_pretrained(model_name, add_pooling_layer=False)
+            processor = ViTImageProcessor.from_pretrained(model_name)
+            return model, processor
+        except requests.exceptions.ProxyError as err:
+            if proxy_error_retries > 0:
+                print(f"Huggingface ProxyError: Retrying in {proxy_error_cooldown} seconds...")
+                import time
+                time.sleep(proxy_error_cooldown)
+                return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown)
+            else:
+                raise err

lrm/models/generator.py

@@ -0,0 +1,116 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch.nn as nn
+
+from .encoders.dino_wrapper import DinoWrapper
+from .transformer import TriplaneTransformer
+from .rendering.synthesizer import TriplaneSynthesizer
+
+
+class CameraEmbedder(nn.Module):
+    """
+    Embed camera features to a high-dimensional vector.
+    
+    Reference:
+    DiT: https://github.com/facebookresearch/DiT/blob/main/models.py#L27
+    """
+    def __init__(self, raw_dim: int, embed_dim: int):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(raw_dim, embed_dim),
+            nn.SiLU(),
+            nn.Linear(embed_dim, embed_dim),
+        )
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class LRMGenerator(nn.Module):
+    """
+    Full model of the large reconstruction model.
+    """
+    def __init__(self, camera_embed_dim: int, rendering_samples_per_ray: int,
+                 transformer_dim: int, transformer_layers: int, transformer_heads: int,
+                 triplane_low_res: int, triplane_high_res: int, triplane_dim: int,
+                 encoder_freeze: bool = True, encoder_model_name: str = 'facebook/dino-vitb16', encoder_feat_dim: int = 768):
+        super().__init__()
+        
+        # attributes
+        self.encoder_feat_dim = encoder_feat_dim
+        self.camera_embed_dim = camera_embed_dim
+
+        # modules
+        self.encoder = DinoWrapper(
+            model_name=encoder_model_name,
+            freeze=encoder_freeze,
+        )
+        self.camera_embedder = CameraEmbedder(
+            raw_dim=12+4, embed_dim=camera_embed_dim,
+        )
+        self.transformer = TriplaneTransformer(
+            inner_dim=transformer_dim, num_layers=transformer_layers, num_heads=transformer_heads,
+            image_feat_dim=encoder_feat_dim,
+            camera_embed_dim=camera_embed_dim,
+            triplane_low_res=triplane_low_res, triplane_high_res=triplane_high_res, triplane_dim=triplane_dim,
+        )
+        self.synthesizer = TriplaneSynthesizer(
+            triplane_dim=triplane_dim, samples_per_ray=rendering_samples_per_ray,
+        )
+
+    def forward_planes(self, image, camera):
+        # image: [N, C_img, H_img, W_img]
+        # camera: [N, D_cam_raw]
+        assert image.shape[0] == camera.shape[0], "Batch size mismatch for image and camera"
+        N = image.shape[0]
+
+        # encode image
+        image_feats = self.encoder(image)
+        assert image_feats.shape[-1] == self.encoder_feat_dim, \
+            f"Feature dimension mismatch: {image_feats.shape[-1]} vs {self.encoder_feat_dim}"
+
+        # embed camera
+        camera_embeddings = self.camera_embedder(camera)
+        assert camera_embeddings.shape[-1] == self.camera_embed_dim, \
+            f"Feature dimension mismatch: {camera_embeddings.shape[-1]} vs {self.camera_embed_dim}"
+
+        # transformer generating planes
+        planes = self.transformer(image_feats, camera_embeddings)
+        assert planes.shape[0] == N, "Batch size mismatch for planes"
+        assert planes.shape[1] == 3, "Planes should have 3 channels"
+
+        return planes
+
+    def forward(self, image, source_camera, render_cameras, render_size: int):
+        # image: [N, C_img, H_img, W_img]
+        # source_camera: [N, D_cam_raw]
+        # render_cameras: [N, M, D_cam_render]
+        # render_size: int
+        assert image.shape[0] == source_camera.shape[0], "Batch size mismatch for image and source_camera"
+        assert image.shape[0] == render_cameras.shape[0], "Batch size mismatch for image and render_cameras"
+        N, M = render_cameras.shape[:2]
+
+        planes = self.forward_planes(image, source_camera)
+
+        # render target views
+        render_results = self.synthesizer(planes, render_cameras, render_size)
+        assert render_results['images_rgb'].shape[0] == N, "Batch size mismatch for render_results"
+        assert render_results['images_rgb'].shape[1] == M, "Number of rendered views should be consistent with render_cameras"
+
+        return {
+            'planes': planes,
+            **render_results,
+        }

lrm/models/rendering/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Empty

lrm/models/rendering/synthesizer.py

@@ -0,0 +1,191 @@
+# ORIGINAL LICENSE
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# Modified by Zexin He
+# The modifications are subject to the same license as the original.
+
+
+import itertools
+import torch
+import torch.nn as nn
+
+from .utils.renderer import ImportanceRenderer
+from .utils.ray_sampler import RaySampler
+
+
+class OSGDecoder(nn.Module):
+    """
+    Triplane decoder that gives RGB and sigma values from sampled features.
+    Using ReLU here instead of Softplus in the original implementation.
+    
+    Reference:
+    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112
+    """
+    def __init__(self, n_features: int,
+                 hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(3 * n_features, hidden_dim),
+            activation(),
+            *itertools.chain(*[[
+                nn.Linear(hidden_dim, hidden_dim),
+                activation(),
+            ] for _ in range(num_layers - 2)]),
+            nn.Linear(hidden_dim, 1 + 3),
+        )
+        # init all bias to zero
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.zeros_(m.bias)
+
+    def forward(self, sampled_features, ray_directions):
+        # Aggregate features by mean
+        # sampled_features = sampled_features.mean(1)
+        # Aggregate features by concatenation
+        _N, n_planes, _M, _C = sampled_features.shape
+        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
+        x = sampled_features
+
+        N, M, C = x.shape
+        x = x.contiguous().view(N*M, C)
+
+        x = self.net(x)
+        x = x.view(N, M, -1)
+        rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
+        sigma = x[..., 0:1]
+
+        return {'rgb': rgb, 'sigma': sigma}
+
+
+class TriplaneSynthesizer(nn.Module):
+    """
+    Synthesizer that renders a triplane volume with planes and a camera.
+    
+    Reference:
+    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19
+    """
+
+    DEFAULT_RENDERING_KWARGS = {
+        'ray_start': 'auto',
+        'ray_end': 'auto',
+        'box_warp': 2.,
+        'white_back': True,
+        'disparity_space_sampling': False,
+        'clamp_mode': 'softplus',
+        'sampler_bbox_min': -1.,
+        'sampler_bbox_max': 1.,
+    }
+
+    def __init__(self, triplane_dim: int, samples_per_ray: int):
+        super().__init__()
+
+        # attributes
+        self.triplane_dim = triplane_dim
+        self.rendering_kwargs = {
+            **self.DEFAULT_RENDERING_KWARGS,
+            'depth_resolution': samples_per_ray // 2,
+            'depth_resolution_importance': samples_per_ray // 2,
+        }
+
+        # renderings
+        self.renderer = ImportanceRenderer()
+        self.ray_sampler = RaySampler()
+
+        # modules
+        self.decoder = OSGDecoder(n_features=triplane_dim)
+
+    def forward(self, planes, cameras, render_size: int):
+        # planes: (N, 3, D', H', W')
+        # cameras: (N, M, D_cam)
+        # render_size: int
+        assert planes.shape[0] == cameras.shape[0], "Batch size mismatch for planes and cameras"
+        N, M = cameras.shape[:2]
+        
+        cam2world_matrix = cameras[..., :16].view(N, M, 4, 4)
+        intrinsics = cameras[..., 16:25].view(N, M, 3, 3)
+
+        # Create a batch of rays for volume rendering
+        ray_origins, ray_directions = self.ray_sampler(
+            cam2world_matrix=cam2world_matrix.reshape(-1, 4, 4),
+            intrinsics=intrinsics.reshape(-1, 3, 3),
+            render_size=render_size,
+        )
+        assert N*M == ray_origins.shape[0], "Batch size mismatch for ray_origins"
+        assert ray_origins.dim() == 3, "ray_origins should be 3-dimensional"
+
+        # Perform volume rendering
+        rgb_samples, depth_samples, weights_samples = self.renderer(
+            planes.repeat_interleave(M, dim=0), self.decoder, ray_origins, ray_directions, self.rendering_kwargs,
+        )
+
+        # Reshape into 'raw' neural-rendered image
+        Himg = Wimg = render_size
+        rgb_images = rgb_samples.permute(0, 2, 1).reshape(N, M, rgb_samples.shape[-1], Himg, Wimg).contiguous()
+        depth_images = depth_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg)
+        weight_images = weights_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg)
+
+        return {
+            'images_rgb': rgb_images,
+            'images_depth': depth_images,
+            'images_weight': weight_images,
+        }
+
+    def forward_grid(self, planes, grid_size: int, aabb: torch.Tensor = None):
+        # planes: (N, 3, D', H', W')
+        # grid_size: int
+        # aabb: (N, 2, 3)
+        if aabb is None:
+            aabb = torch.tensor([
+                [self.rendering_kwargs['sampler_bbox_min']] * 3,
+                [self.rendering_kwargs['sampler_bbox_max']] * 3,
+            ], device=planes.device, dtype=planes.dtype).unsqueeze(0).repeat(planes.shape[0], 1, 1)
+        assert planes.shape[0] == aabb.shape[0], "Batch size mismatch for planes and aabb"
+        N = planes.shape[0]
+
+        # create grid points for triplane query
+        grid_points = []
+        for i in range(N):
+            grid_points.append(torch.stack(torch.meshgrid(
+                torch.linspace(aabb[i, 0, 0], aabb[i, 1, 0], grid_size, device=planes.device),
+                torch.linspace(aabb[i, 0, 1], aabb[i, 1, 1], grid_size, device=planes.device),
+                torch.linspace(aabb[i, 0, 2], aabb[i, 1, 2], grid_size, device=planes.device),
+                indexing='ij',
+            ), dim=-1).reshape(-1, 3))
+        cube_grid = torch.stack(grid_points, dim=0).to(planes.device)
+
+        features = self.forward_points(planes, cube_grid)
+
+        # reshape into grid
+        features = {
+            k: v.reshape(N, grid_size, grid_size, grid_size, -1)
+            for k, v in features.items()
+        }
+        return features
+
+    def forward_points(self, planes, points: torch.Tensor, chunk_size: int = 2**20):
+        # planes: (N, 3, D', H', W')
+        # points: (N, P, 3)
+        N, P = points.shape[:2]
+
+        # query triplane in chunks
+        outs = []
+        for i in range(0, points.shape[1], chunk_size):
+            chunk_points = points[:, i:i+chunk_size]
+
+            # query triplane
+            chunk_out = self.renderer.run_model_activated(
+                planes=planes,
+                decoder=self.decoder,
+                sample_coordinates=chunk_points,
+                sample_directions=torch.zeros_like(chunk_points),
+                options=self.rendering_kwargs,
+            )
+            outs.append(chunk_out)
+
+        # concatenate the outputs
+        point_features = {
+            k: torch.cat([out[k] for out in outs], dim=1)
+            for k in outs[0].keys()
+        }
+        return point_features

lrm/models/rendering/utils/__init__.py

@@ -0,0 +1,9 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.

lrm/models/rendering/utils/math_utils.py

@@ -0,0 +1,118 @@
+# MIT License
+
+# Copyright (c) 2022 Petr Kellnhofer
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import torch
+
+def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor:
+    """
+    Left-multiplies MxM @ NxM. Returns NxM.
+    """
+    res = torch.matmul(vectors4, matrix.T)
+    return res
+
+
+def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
+    """
+    Normalize vector lengths.
+    """
+    return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
+
+def torch_dot(x: torch.Tensor, y: torch.Tensor):
+    """
+    Dot product of two tensors.
+    """
+    return (x * y).sum(-1)
+
+
+def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length):
+    """
+    Author: Petr Kellnhofer
+    Intersects rays with the [-1, 1] NDC volume.
+    Returns min and max distance of entry.
+    Returns -1 for no intersection.
+    https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
+    """
+    o_shape = rays_o.shape
+    rays_o = rays_o.detach().reshape(-1, 3)
+    rays_d = rays_d.detach().reshape(-1, 3)
+
+
+    bb_min = [-1*(box_side_length/2), -1*(box_side_length/2), -1*(box_side_length/2)]
+    bb_max = [1*(box_side_length/2), 1*(box_side_length/2), 1*(box_side_length/2)]
+    bounds = torch.tensor([bb_min, bb_max], dtype=rays_o.dtype, device=rays_o.device)
+    is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)
+
+    # Precompute inverse for stability.
+    invdir = 1 / rays_d
+    sign = (invdir < 0).long()
+
+    # Intersect with YZ plane.
+    tmin = (bounds.index_select(0, sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
+    tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
+
+    # Intersect with XZ plane.
+    tymin = (bounds.index_select(0, sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
+    tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
+
+    # Resolve parallel rays.
+    is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False
+
+    # Use the shortest intersection.
+    tmin = torch.max(tmin, tymin)
+    tmax = torch.min(tmax, tymax)
+
+    # Intersect with XY plane.
+    tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
+    tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
+
+    # Resolve parallel rays.
+    is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False
+
+    # Use the shortest intersection.
+    tmin = torch.max(tmin, tzmin)
+    tmax = torch.min(tmax, tzmax)
+
+    # Mark invalid.
+    tmin[torch.logical_not(is_valid)] = -1
+    tmax[torch.logical_not(is_valid)] = -2
+
+    return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)
+
+
+def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
+    """
+    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
+    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
+    """
+    # create a tensor of 'num' steps from 0 to 1
+    steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)
+
+    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
+    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
+    #   "cannot statically infer the expected size of a list in this contex", hence the code below
+    for i in range(start.ndim):
+        steps = steps.unsqueeze(-1)
+
+    # the output starts at 'start' and increments until 'stop' in each dimension
+    out = start[None] + steps * (stop - start)[None]
+
+    return out

lrm/models/rendering/utils/ray_marcher.py

@@ -0,0 +1,65 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+# Modified by Zexin He
+# The modifications are subject to the same license as the original.
+
+
+"""
+The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths.
+Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!)
+"""
+
+import torch
+import torch.nn as nn
+
+
+class MipRayMarcher2(nn.Module):
+    def __init__(self, activation_factory):
+        super().__init__()
+        self.activation_factory = activation_factory
+
+    def run_forward(self, colors, densities, depths, rendering_options):
+        deltas = depths[:, :, 1:] - depths[:, :, :-1]
+        colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2
+        densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2
+        depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2
+
+        # using factory mode for better usability
+        densities_mid = self.activation_factory(rendering_options)(densities_mid)
+
+        density_delta = densities_mid * deltas
+
+        alpha = 1 - torch.exp(-density_delta)
+
+        alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2)
+        weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]
+
+        composite_rgb = torch.sum(weights * colors_mid, -2)
+        weight_total = weights.sum(2)
+        composite_depth = torch.sum(weights * depths_mid, -2) / weight_total
+
+        # clip the composite to min/max range of depths
+        composite_depth = torch.nan_to_num(composite_depth, float('inf'))
+        composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths))
+
+        if rendering_options.get('white_back', False):
+            composite_rgb = composite_rgb + 1 - weight_total
+
+        # rendered value scale is 0-1, comment out original mipnerf scaling
+        # composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1)
+
+        return composite_rgb, composite_depth, weights
+
+
+    def forward(self, colors, densities, depths, rendering_options):
+        composite_rgb, composite_depth, weights = self.run_forward(colors, densities, depths, rendering_options)
+
+        return composite_rgb, composite_depth, weights

lrm/models/rendering/utils/ray_sampler.py

@@ -0,0 +1,81 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+# Modified by Zexin He
+# The modifications are subject to the same license as the original.
+
+
+"""
+The ray sampler is a module that takes in camera matrices and resolution and batches of rays.
+Expects cam2world matrices that use the OpenCV camera coordinate system conventions.
+"""
+
+import torch
+
+class RaySampler(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
+
+
+    def forward(self, cam2world_matrix, intrinsics, render_size):
+        """
+        Create batches of rays and return origins and directions.
+
+        cam2world_matrix: (N, 4, 4)
+        intrinsics: (N, 3, 3)
+        render_size: int
+
+        ray_origins: (N, M, 3)
+        ray_dirs: (N, M, 2)
+        """
+
+        N, M = cam2world_matrix.shape[0], render_size**2
+        cam_locs_world = cam2world_matrix[:, :3, 3]
+        fx = intrinsics[:, 0, 0]
+        fy = intrinsics[:, 1, 1]
+        cx = intrinsics[:, 0, 2]
+        cy = intrinsics[:, 1, 2]
+        sk = intrinsics[:, 0, 1]
+
+        uv = torch.stack(torch.meshgrid(
+            torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
+            torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
+            indexing='ij',
+        ))
+        uv = uv.flip(0).reshape(2, -1).transpose(1, 0)
+        uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
+
+        x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size)
+        y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size)
+        z_cam = torch.ones((N, M), device=cam2world_matrix.device)
+
+        x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y_cam/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
+        y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
+
+        cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
+
+        _opencv2blender = torch.tensor([
+            [1, 0, 0, 0],
+            [0, -1, 0, 0],
+            [0, 0, -1, 0],
+            [0, 0, 0, 1],
+        ], dtype=torch.float32, device=cam2world_matrix.device).unsqueeze(0).repeat(N, 1, 1)
+
+        cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender)
+
+        world_rel_points = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3]
+
+        ray_dirs = world_rel_points - cam_locs_world[:, None, :]
+        ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2)
+
+        ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1)
+
+        return ray_origins, ray_dirs

lrm/models/rendering/utils/renderer.py

@@ -0,0 +1,303 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+# Modified by Zexin He
+# The modifications are subject to the same license as the original.
+
+
+"""
+The renderer is a module that takes in rays, decides where to sample along each
+ray, and computes pixel colors using the volume rendering equation.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .ray_marcher import MipRayMarcher2
+from . import math_utils
+
+def generate_planes():
+    """
+    Defines planes by the three vectors that form the "axes" of the
+    plane. Should work with arbitrary number of planes and planes of
+    arbitrary orientation.
+
+    Bugfix reference: https://github.com/NVlabs/eg3d/issues/67
+    """
+    return torch.tensor([[[1, 0, 0],
+                            [0, 1, 0],
+                            [0, 0, 1]],
+                            [[1, 0, 0],
+                            [0, 0, 1],
+                            [0, 1, 0]],
+                            [[0, 0, 1],
+                            [0, 1, 0],
+                            [1, 0, 0]]], dtype=torch.float32)
+
+def project_onto_planes(planes, coordinates):
+    """
+    Does a projection of a 3D point onto a batch of 2D planes,
+    returning 2D plane coordinates.
+
+    Takes plane axes of shape n_planes, 3, 3
+    # Takes coordinates of shape N, M, 3
+    # returns projections of shape N*n_planes, M, 2
+    """
+    N, M, C = coordinates.shape
+    n_planes, _, _ = planes.shape
+    coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3)
+    inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3)
+    projections = torch.bmm(coordinates, inv_planes)
+    return projections[..., :2]
+
+def sample_from_planes(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None):
+    assert padding_mode == 'zeros'
+    N, n_planes, C, H, W = plane_features.shape
+    _, M, _ = coordinates.shape
+    plane_features = plane_features.view(N*n_planes, C, H, W)
+
+    coordinates = (2/box_warp) * coordinates # add specific box bounds
+
+    projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1)
+    output_features = torch.nn.functional.grid_sample(plane_features, projected_coordinates.float(), mode=mode, padding_mode=padding_mode, align_corners=False).permute(0, 3, 2, 1).reshape(N, n_planes, M, C)
+    return output_features
+
+def sample_from_3dgrid(grid, coordinates):
+    """
+    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
+    Expects grid in shape (1, channels, H, W, D)
+    (Also works if grid has batch size)
+    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
+    """
+    batch_size, n_coords, n_dims = coordinates.shape
+    sampled_features = torch.nn.functional.grid_sample(grid.expand(batch_size, -1, -1, -1, -1),
+                                                       coordinates.reshape(batch_size, 1, 1, -1, n_dims),
+                                                       mode='bilinear', padding_mode='zeros', align_corners=False)
+    N, C, H, W, D = sampled_features.shape
+    sampled_features = sampled_features.permute(0, 4, 3, 2, 1).reshape(N, H*W*D, C)
+    return sampled_features
+
+class ImportanceRenderer(torch.nn.Module):
+    """
+    Modified original version to filter out-of-box samples as TensoRF does.
+    
+    Reference:
+    TensoRF: https://github.com/apchenstu/TensoRF/blob/main/models/tensorBase.py#L277
+    """
+    def __init__(self):
+        super().__init__()
+        self.activation_factory = self._build_activation_factory()
+        self.ray_marcher = MipRayMarcher2(self.activation_factory)
+        self.plane_axes = generate_planes()
+
+    def _build_activation_factory(self):
+        def activation_factory(options: dict):
+            if options['clamp_mode'] == 'softplus':
+                return lambda x: F.softplus(x - 1)  # activation bias of -1 makes things initialize better
+            else:
+                assert False, "Renderer only supports `clamp_mode`=`softplus`!"
+        return activation_factory
+
+    def _forward_pass(self, depths: torch.Tensor, ray_directions: torch.Tensor, ray_origins: torch.Tensor,
+                        planes: torch.Tensor, decoder: nn.Module, rendering_options: dict):
+        """
+        Additional filtering is applied to filter out-of-box samples.
+        Modifications made by Zexin He.
+        """
+
+        # context related variables
+        batch_size, num_rays, samples_per_ray, _ = depths.shape
+        device = depths.device
+
+        # define sample points with depths
+        sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
+        sample_coordinates = (ray_origins.unsqueeze(-2) + depths * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)
+
+        # filter out-of-box samples
+        mask_inbox = \
+            (rendering_options['sampler_bbox_min'] <= sample_coordinates) & \
+                (sample_coordinates <= rendering_options['sampler_bbox_max'])
+        mask_inbox = mask_inbox.all(-1)
+
+        # forward model according to all samples
+        _out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options)
+
+        # set out-of-box samples to zeros(rgb) & -inf(sigma)
+        SAFE_GUARD = 3
+        DATA_TYPE = _out['sigma'].dtype
+        colors_pass = torch.zeros(batch_size, num_rays * samples_per_ray, 3, device=device, dtype=DATA_TYPE)
+        densities_pass = torch.nan_to_num(torch.full((batch_size, num_rays * samples_per_ray, 1), -float('inf'), device=device, dtype=DATA_TYPE)) / SAFE_GUARD
+        colors_pass[mask_inbox], densities_pass[mask_inbox] = _out['rgb'][mask_inbox], _out['sigma'][mask_inbox]
+
+        # reshape back
+        colors_pass = colors_pass.reshape(batch_size, num_rays, samples_per_ray, colors_pass.shape[-1])
+        densities_pass = densities_pass.reshape(batch_size, num_rays, samples_per_ray, densities_pass.shape[-1])
+
+        return colors_pass, densities_pass
+
+    def forward(self, planes, decoder, ray_origins, ray_directions, rendering_options):
+        # self.plane_axes = self.plane_axes.to(ray_origins.device)
+
+        if rendering_options['ray_start'] == rendering_options['ray_end'] == 'auto':
+            ray_start, ray_end = math_utils.get_ray_limits_box(ray_origins, ray_directions, box_side_length=rendering_options['box_warp'])
+            is_ray_valid = ray_end > ray_start
+            if torch.any(is_ray_valid).item():
+                ray_start[~is_ray_valid] = ray_start[is_ray_valid].min()
+                ray_end[~is_ray_valid] = ray_start[is_ray_valid].max()
+            depths_coarse = self.sample_stratified(ray_origins, ray_start, ray_end, rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
+        else:
+            # Create stratified depth samples
+            depths_coarse = self.sample_stratified(ray_origins, rendering_options['ray_start'], rendering_options['ray_end'], rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
+
+        # Coarse Pass
+        colors_coarse, densities_coarse = self._forward_pass(
+            depths=depths_coarse, ray_directions=ray_directions, ray_origins=ray_origins,
+            planes=planes, decoder=decoder, rendering_options=rendering_options)
+
+        # Fine Pass
+        N_importance = rendering_options['depth_resolution_importance']
+        if N_importance > 0:
+            _, _, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)
+
+            depths_fine = self.sample_importance(depths_coarse, weights, N_importance)
+
+            colors_fine, densities_fine = self._forward_pass(
+                depths=depths_fine, ray_directions=ray_directions, ray_origins=ray_origins,
+                planes=planes, decoder=decoder, rendering_options=rendering_options)
+
+            all_depths, all_colors, all_densities = self.unify_samples(depths_coarse, colors_coarse, densities_coarse,
+                                                                  depths_fine, colors_fine, densities_fine)
+
+            # Aggregate
+            rgb_final, depth_final, weights = self.ray_marcher(all_colors, all_densities, all_depths, rendering_options)
+        else:
+            rgb_final, depth_final, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)
+
+        return rgb_final, depth_final, weights.sum(2)
+
+    def run_model(self, planes, decoder, sample_coordinates, sample_directions, options):
+        plane_axes = self.plane_axes.to(planes.device)
+        sampled_features = sample_from_planes(plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp'])
+
+        out = decoder(sampled_features, sample_directions)
+        if options.get('density_noise', 0) > 0:
+            out['sigma'] += torch.randn_like(out['sigma']) * options['density_noise']
+        return out
+
+    def run_model_activated(self, planes, decoder, sample_coordinates, sample_directions, options):
+        out = self.run_model(planes, decoder, sample_coordinates, sample_directions, options)
+        out['sigma'] = self.activation_factory(options)(out['sigma'])
+        return out
+
+    def sort_samples(self, all_depths, all_colors, all_densities):
+        _, indices = torch.sort(all_depths, dim=-2)
+        all_depths = torch.gather(all_depths, -2, indices)
+        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
+        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))
+        return all_depths, all_colors, all_densities
+
+    def unify_samples(self, depths1, colors1, densities1, depths2, colors2, densities2):
+        all_depths = torch.cat([depths1, depths2], dim = -2)
+        all_colors = torch.cat([colors1, colors2], dim = -2)
+        all_densities = torch.cat([densities1, densities2], dim = -2)
+
+        _, indices = torch.sort(all_depths, dim=-2)
+        all_depths = torch.gather(all_depths, -2, indices)
+        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
+        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))
+
+        return all_depths, all_colors, all_densities
+
+    def sample_stratified(self, ray_origins, ray_start, ray_end, depth_resolution, disparity_space_sampling=False):
+        """
+        Return depths of approximately uniformly spaced samples along rays.
+        """
+        N, M, _ = ray_origins.shape
+        if disparity_space_sampling:
+            depths_coarse = torch.linspace(0,
+                                    1,
+                                    depth_resolution,
+                                    device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
+            depth_delta = 1/(depth_resolution - 1)
+            depths_coarse += torch.rand_like(depths_coarse) * depth_delta
+            depths_coarse = 1./(1./ray_start * (1. - depths_coarse) + 1./ray_end * depths_coarse)
+        else:
+            if type(ray_start) == torch.Tensor:
+                depths_coarse = math_utils.linspace(ray_start, ray_end, depth_resolution).permute(1,2,0,3)
+                depth_delta = (ray_end - ray_start) / (depth_resolution - 1)
+                depths_coarse += torch.rand_like(depths_coarse) * depth_delta[..., None]
+            else:
+                depths_coarse = torch.linspace(ray_start, ray_end, depth_resolution, device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
+                depth_delta = (ray_end - ray_start)/(depth_resolution - 1)
+                depths_coarse += torch.rand_like(depths_coarse) * depth_delta
+
+        return depths_coarse
+
+    def sample_importance(self, z_vals, weights, N_importance):
+        """
+        Return depths of importance sampled points along rays. See NeRF importance sampling for more.
+        """
+        with torch.no_grad():
+            batch_size, num_rays, samples_per_ray, _ = z_vals.shape
+
+            z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray)
+            weights = weights.reshape(batch_size * num_rays, -1) # -1 to account for loss of 1 sample in MipRayMarcher
+
+            # smooth weights
+            weights = torch.nn.functional.max_pool1d(weights.unsqueeze(1).float(), 2, 1, padding=1)
+            weights = torch.nn.functional.avg_pool1d(weights, 2, 1).squeeze()
+            weights = weights + 0.01
+
+            z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:])
+            importance_z_vals = self.sample_pdf(z_vals_mid, weights[:, 1:-1],
+                                             N_importance).detach().reshape(batch_size, num_rays, N_importance, 1)
+        return importance_z_vals
+
+    def sample_pdf(self, bins, weights, N_importance, det=False, eps=1e-5):
+        """
+        Sample @N_importance samples from @bins with distribution defined by @weights.
+        Inputs:
+            bins: (N_rays, N_samples_+1) where N_samples_ is "the number of coarse samples per ray - 2"
+            weights: (N_rays, N_samples_)
+            N_importance: the number of samples to draw from the distribution
+            det: deterministic or not
+            eps: a small number to prevent division by zero
+        Outputs:
+            samples: the sampled samples
+        """
+        N_rays, N_samples_ = weights.shape
+        weights = weights + eps # prevent division by zero (don't do inplace op!)
+        pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)
+        cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function
+        cdf = torch.cat([torch.zeros_like(cdf[: ,:1]), cdf], -1)  # (N_rays, N_samples_+1)
+                                                                   # padded to 0~1 inclusive
+
+        if det:
+            u = torch.linspace(0, 1, N_importance, device=bins.device)
+            u = u.expand(N_rays, N_importance)
+        else:
+            u = torch.rand(N_rays, N_importance, device=bins.device)
+        u = u.contiguous()
+
+        inds = torch.searchsorted(cdf, u, right=True)
+        below = torch.clamp_min(inds-1, 0)
+        above = torch.clamp_max(inds, N_samples_)
+
+        inds_sampled = torch.stack([below, above], -1).view(N_rays, 2*N_importance)
+        cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, N_importance, 2)
+        bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)
+
+        denom = cdf_g[...,1]-cdf_g[...,0]
+        denom[denom<eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled
+                             # anyway, therefore any value for it is fine (set to 1 here)
+
+        samples = bins_g[...,0] + (u-cdf_g[...,0])/denom * (bins_g[...,1]-bins_g[...,0])
+        return samples

lrm/models/transformer.py

@@ -0,0 +1,143 @@
+# Copyright (c) 2023, Zexin He
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+import torch.nn as nn
+
+
+class ModLN(nn.Module):
+    """
+    Modulation with adaLN.
+    
+    References:
+    DiT: https://github.com/facebookresearch/DiT/blob/main/models.py#L101
+    """
+    def __init__(self, inner_dim: int, mod_dim: int, eps: float):
+        super().__init__()
+        self.norm = nn.LayerNorm(inner_dim, eps=eps)
+        self.mlp = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(mod_dim, inner_dim * 2),
+        )
+
+    @staticmethod
+    def modulate(x, shift, scale):
+        # x: [N, L, D]
+        # shift, scale: [N, D]
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+    def forward(self, x, cond):
+        shift, scale = self.mlp(cond).chunk(2, dim=-1)  # [N, D]
+        return self.modulate(self.norm(x), shift, scale)  # [N, L, D]
+
+
+class ConditionModulationBlock(nn.Module):
+    """
+    Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
+    """
+    # use attention from torch.nn.MultiHeadAttention
+    # Block contains a cross-attention layer, a self-attention layer, and a MLP
+    def __init__(self, inner_dim: int, cond_dim: int, mod_dim: int, num_heads: int, eps: float,
+                 attn_drop: float = 0., attn_bias: bool = False,
+                 mlp_ratio: float = 4., mlp_drop: float = 0.):
+        super().__init__()
+        self.norm1 = ModLN(inner_dim, mod_dim, eps)
+        self.cross_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm2 = ModLN(inner_dim, mod_dim, eps)
+        self.self_attn = nn.MultiheadAttention(
+            embed_dim=inner_dim, num_heads=num_heads,
+            dropout=attn_drop, bias=attn_bias, batch_first=True)
+        self.norm3 = ModLN(inner_dim, mod_dim, eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
+            nn.GELU(),
+            nn.Dropout(mlp_drop),
+            nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
+            nn.Dropout(mlp_drop),
+        )
+
+    def forward(self, x, cond, mod):
+        # x: [N, L, D]
+        # cond: [N, L_cond, D_cond]
+        # mod: [N, D_mod]
+        x = x + self.cross_attn(self.norm1(x, mod), cond, cond)[0]
+        before_sa = self.norm2(x, mod)
+        x = x + self.self_attn(before_sa, before_sa, before_sa)[0]
+        x = x + self.mlp(self.norm3(x, mod))
+        return x
+
+
+class TriplaneTransformer(nn.Module):
+    """
+    Transformer with condition and modulation that generates a triplane representation.
+    
+    Reference:
+    Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486
+    """
+    def __init__(self, inner_dim: int, image_feat_dim: int, camera_embed_dim: int,
+                 triplane_low_res: int, triplane_high_res: int, triplane_dim: int,
+                 num_layers: int, num_heads: int,
+                 eps: float = 1e-6):
+        super().__init__()
+
+        # attributes
+        self.triplane_low_res = triplane_low_res
+        self.triplane_high_res = triplane_high_res
+        self.triplane_dim = triplane_dim
+
+        # modules
+        # initialize pos_embed with 1/sqrt(dim) * N(0, 1)
+        self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5)
+        self.layers = nn.ModuleList([
+            ConditionModulationBlock(
+                inner_dim=inner_dim, cond_dim=image_feat_dim, mod_dim=camera_embed_dim, num_heads=num_heads, eps=eps)
+            for _ in range(num_layers)
+        ])
+        self.norm = nn.LayerNorm(inner_dim, eps=eps)
+        self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0)
+
+    def forward(self, image_feats, camera_embeddings):
+        # image_feats: [N, L_cond, D_cond]
+        # camera_embeddings: [N, D_mod]
+
+        assert image_feats.shape[0] == camera_embeddings.shape[0], \
+            f"Mismatched batch size: {image_feats.shape[0]} vs {camera_embeddings.shape[0]}"
+
+        N = image_feats.shape[0]
+        H = W = self.triplane_low_res
+        L = 3 * H * W
+
+        x = self.pos_embed.repeat(N, 1, 1)  # [N, L, D]
+        for layer in self.layers:
+            x = layer(x, image_feats, camera_embeddings)
+        x = self.norm(x)
+
+        # separate each plane and apply deconv
+        x = x.view(N, 3, H, W, -1)
+        x = torch.einsum('nihwd->indhw', x)  # [3, N, D, H, W]
+        x = x.contiguous().view(3*N, -1, H, W)  # [3*N, D, H, W]
+        x = self.deconv(x)  # [3*N, D', H', W']
+        x = x.view(3, N, *x.shape[-3:])  # [3, N, D', H', W']
+        x = torch.einsum('indhw->nidhw', x)  # [N, 3, D', H', W']
+        x = x.contiguous()
+
+        assert self.triplane_high_res == x.shape[-2], \
+            f"Output triplane resolution does not match with expected: {x.shape[-2]} vs {self.triplane_high_res}"
+        assert self.triplane_dim == x.shape[-3], \
+            f"Output triplane dimension does not match with expected: {x.shape[-3]} vs {self.triplane_dim}"
+
+        return x

requirements.txt

@@ -0,0 +1,7 @@
+torch>=2.1.0
+transformers
+omegaconf
+pillow
+imageio[ffmpeg]
+PyMCubes
+trimesh