update and include model files

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <[email protected]>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to PoseDiffusion
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to PoseDiffusion, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

@@ -0,0 +1,399 @@
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README.md

@@ -1,199 +1,89 @@
----
-library_name: transformers
-tags: []
----
+# [ECCV 2024] VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models
 
-# Model Card for Model ID
+[Porject page](https://junlinhan.github.io/projects/vfusion3d.html), [Paper link](https://arxiv.org/abs/2403.12034)
 
-<!-- Provide a quick summary of what the model is/does. -->
+VFusion3D is a large, feed-forward 3D generative model trained with a small amount of 3D data and a large volume of synthetic multi-view data. It is the first work exploring scalable 3D generative/reconstruction models as a step towards a 3D foundation.
 
+[VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models](https://junlinhan.github.io/projects/vfusion3d.html)<br>
+[Junlin Han](https://junlinhan.github.io/), [Filippos Kokkinos](https://www.fkokkinos.com/), [Philip Torr](https://www.robots.ox.ac.uk/~phst/)<br>
+GenAI, Meta and TVG, University of Oxford<br>
+European Conference on Computer Vision (ECCV), 2024
 
 
-## Model Details
+## News
 
-### Model Description
+- [25.07.2024] Release weights and inference code for VFusion3D.
 
-<!-- Provide a longer summary of what this model is. -->
+## Results and Comparisons
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### 3D Generation Results
+<img src='images/gif1.gif' width=950>
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+<img src='images/gif2.gif' width=950>
 
-### Model Sources [optional]
+### User Study Results
+<img src='images/user.png' width=950>
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Setup
 
-## Uses
+### Installation
+```
+git clone https://github.com/facebookresearch/vfusion3d
+cd vfusion3d
+```
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Environment
+We provide a simple installation script that, by default, sets up a conda environment with Python 3.8.19, PyTorch 2.3, and CUDA 12.1. Similar package versions should also work.
 
-### Direct Use
+```
+source install.sh
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Quick Start
 
-[More Information Needed]
+### Pretrained Models
 
-### Downstream Use [optional]
+- Model weights are available here [Google Drive](https://drive.google.com/file/d/1b-KKSh9VquJdzmXzZBE4nKbXnbeua42X/view?usp=sharing). Please download it and put it inside ./checkpoints/
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+### Prepare Images
+- We put some sample inputs under `assets/40_prompt_images`, which is the 40 MVDream prompt images used in the paper. Results of them are also provided under `results/40_prompt_images_provided`. 
 
-### Out-of-Scope Use
+### Inference
+- Run the inference script to get 3D assets.
+- You may specify which form of output to generate by setting the flags `--export_video` and `--export_mesh`.
+- Change `--source_path` and `--dump_path` if you want to run it on other image folders. 
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+    ```
+    # Example usages
+    # Render a video
+    python -m lrm.inferrer --export_video --resume ./checkpoints/vfusion3dckpt
+    
+    # Export mesh
+    python -m lrm.inferrer --export_mesh --resume ./checkpoints/vfusion3dckpt
+    ```
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+## Acknowledgement
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- This inference code of VFusion3D heavily borrows from [OpenLRM](https://github.com/3DTopia/OpenLRM).  
 
-[More Information Needed]
+## Citation
 
-### Recommendations
+If you find this work useful, please cite us:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+```
+@article{han2024vfusion3d,
+  title={VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models},
+  author={Junlin Han and Filippos Kokkinos and Philip Torr},
+  journal={European Conference on Computer Vision (ECCV)},
+  year={2024}
+}
+```
 
-## How to Get Started with the Model
+## License
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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+- The majority of VFusion3D is licensed under CC-BY-NC, however portions of the project are available under separate license terms: OpenLRM as a whole is licensed under the Apache License, Version 2.0, while certain components are covered by NVIDIA's proprietary license.
+- The model weights of VFusion3D is also licensed under CC-BY-NC.

__init__.py

@@ -0,0 +1 @@
+from .modeling import LRMGenerator, LRMGeneratorConfig

install.sh

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This Script Assumes Python 3.8.19, CUDA 12.1. Similar package versions might still work but they are not tested.
+
+conda deactivate
+
+# Set environment variables
+export ENV_NAME=vfusion3d
+export PYTHON_VERSION=3.8.19
+export CUDA_VERSION=12.1
+
+# Create a new conda environment and activate it
+conda create -n $ENV_NAME python=$PYTHON_VERSION
+conda activate $ENV_NAME
+conda install pytorch=2.3.0 torchvision==0.18.0 pytorch-cuda=$CUDA_VERSION -c pytorch -c nvidia
+pip install transformers
+pip install imageio[ffmpeg]
+pip install PyMCubes
+pip install trimesh
+pip install rembg[gpu,cli]
+pip install kiui

lrm/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

lrm/cam_utils.py

@@ -0,0 +1,138 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import torch
+import numpy as np
+import math
+
+"""
+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).to(RT.device)
+        ], 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
+
+def get_surrounding_views(M, radius, elevation):
+#   convert spherical coordinates (radius, azimuth, elevation) to Cartesian coordinates (x, y, z).
+    camera_positions = []
+    rand_theta= np.random.uniform(0, np.pi/180)
+    elevation = math.radians(elevation)
+    for i in range(M):
+        theta = 2 * math.pi * i / M  + rand_theta
+        x = radius * math.cos(theta) * math.cos(elevation)
+        y = radius * math.sin(theta) * math.cos(elevation)
+        z =  radius * math.sin(elevation)
+        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

lrm/inferrer.py

@@ -0,0 +1,232 @@
+import torch
+import math
+import os
+import imageio
+import mcubes
+import trimesh
+import numpy as np
+import argparse
+from torchvision.utils import save_image
+from PIL import Image
+import glob
+from .models.generator import LRMGenerator  # Make sure this import is correct
+from .cam_utils import build_camera_principle, build_camera_standard, center_looking_at_camera_pose  # Make sure this import is correct
+from functools import partial
+from rembg import remove, new_session
+from kiui.op import recenter
+import kiui
+
+class LRMInferrer:
+    def __init__(self, model_name: str, resume: str):
+        print("Initializing LRMInferrer")
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        _model_kwargs = {'camera_embed_dim': 1024, 'rendering_samples_per_ray': 128, 'transformer_dim': 1024, 'transformer_layers': 16, 'transformer_heads': 16, 'triplane_low_res': 32, 'triplane_high_res': 64, 'triplane_dim': 80, 'encoder_freeze': False}
+        
+        self.model = self._build_model(_model_kwargs).eval().to(self.device)
+        checkpoint = torch.load(resume, map_location='cpu')
+        state_dict = checkpoint['model_state_dict']
+        self.model.load_state_dict(state_dict)
+        del checkpoint, state_dict
+        torch.cuda.empty_cache()
+
+    def __enter__(self):
+        print("Entering context")
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        print("Exiting context")
+        if exc_type:
+            print(f"Exception type: {exc_type}")
+            print(f"Exception value: {exc_val}")
+            print(f"Traceback: {exc_tb}")
+
+    def _build_model(self, model_kwargs):
+        print("Building model")
+        model = LRMGenerator(**model_kwargs).to(self.device)
+        print("Loaded model from checkpoint")
+        return model
+
+    @staticmethod
+    def get_surrounding_views(M, radius, elevation):
+        camera_positions = []
+        rand_theta = np.random.uniform(0, np.pi/180)
+        elevation = math.radians(elevation)
+        for i in range(M):
+            theta = 2 * math.pi * i / M + rand_theta
+            x = radius * math.cos(theta) * math.cos(elevation)
+            y = radius * math.sin(theta) * math.cos(elevation)
+            z = radius * math.sin(elevation)
+            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():
+        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):
+        dist_to_center = 1.5
+        canonical_camera_extrinsics = torch.tensor([[
+            [0, 0, 1, 1],
+            [1, 0, 0, 0],
+            [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):
+        render_camera_extrinsics = self.get_surrounding_views(160, 1.5, 0)
+        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):
+        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)
+        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):
+        print("infer_single called")
+        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(image, source_camera)
+            results = {}
+
+            if export_video:
+                print("Starting export_video")
+                frames = []
+                for i in range(0, render_cameras.shape[1], chunk_size):
+                    print(f"Processing chunk {i} to {i + chunk_size}")
+                    frames.append(
+                        self.model.synthesizer(
+                            planes,
+                            render_cameras[:, i:i+chunk_size],
+                            render_size,
+                            render_size,
+                            0,
+                            0
+                        )
+                    )
+                frames = {
+                    k: torch.cat([r[k] for r in frames], dim=1)
+                    for k in frames[0].keys()
+                }
+                results.update({
+                    'frames': frames,
+                })
+                print("Finished export_video")
+
+            if export_mesh:
+                print("Starting export_mesh")
+                grid_out = self.model.synthesizer.forward_grid(
+                    planes=planes,
+                    grid_size=mesh_size,
+                )
+                vtx, faces = mcubes.marching_cubes(grid_out['sigma'].float().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'].float().squeeze(0).cpu().numpy()
+                vtx_colors = (vtx_colors * 255).astype(np.uint8)
+                mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+                results.update({
+                    'mesh': mesh,
+                })
+                print("Finished export_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):
+        print("infer called")
+        session = new_session("isnet-general-use")
+        rembg_remove = partial(remove, session=session)
+        image_name = os.path.basename(source_image)
+        uid = image_name.split('.')[0]
+
+        image = kiui.read_image(source_image, mode='uint8')
+        image = rembg_remove(image)
+        mask = rembg_remove(image, only_mask=True)
+        image = recenter(image, mask, border_ratio=0.20)
+        os.makedirs(dump_path, exist_ok=True)
+
+        image = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0) / 255.0
+        if image.shape[1] == 4:
+            image = image[:, :3, ...] * image[:, 3:, ...] + (1 - image[:, 3:, ...])
+        image = torch.nn.functional.interpolate(image, size=(source_size, source_size), mode='bicubic', align_corners=True)
+        image = torch.clamp(image, 0, 1)
+        save_image(image, os.path.join(dump_path, f'{uid}.png'))
+
+        results = self.infer_single(
+            image.cuda(),
+            render_size=render_size,
+            mesh_size=mesh_size,
+            export_video=export_video,
+            export_mesh=export_mesh,
+        )
+
+        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}.mp4'),
+                        fps=40,
+                    )
+                    print(f"Export video success to {dump_path}")
+
+        if 'mesh' in results:
+            mesh = results['mesh']
+            mesh.export(os.path.join(dump_path, f'{uid}.obj'), 'obj')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name', type=str, default='lrm-base-obj-v1')
+    parser.add_argument('--source_path', type=str, default='./assets/cat.png')
+    parser.add_argument('--dump_path', type=str, default='./results/single_image')
+    parser.add_argument('--source_size', type=int, default=512)
+    parser.add_argument('--render_size', type=int, default=384)
+    parser.add_argument('--mesh_size', type=int, default=512)
+    parser.add_argument('--export_video', action='store_true')
+    parser.add_argument('--export_mesh', action='store_true')
+    parser.add_argument('--resume', type=str, required=True, help='Path to a checkpoint to resume training from')
+    args = parser.parse_args()
+
+    with LRMInferrer(model_name=args.model_name, resume=args.resume) as inferrer:
+        with torch.autocast(device_type="cuda", cache_enabled=False, dtype=torch.float32):
+            print("Start inference for image:", args.source_path)
+            inferrer.infer(
+                source_image=args.source_path,
+                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,
+            )
+            print("Finished inference for image:", args.source_path)

lrm/models/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.