Update README.md

README.md

@@ -9,9 +9,12 @@ language:
 # TRELLIS.2: Native and Compact Structured Latents for 3D Generation
 
 **Model Name:** TRELLIS.2-4B
-**Paper:** [Coming Soon]  
-**Repository:** [GitHub Link Placeholder]  
-**Project Page:** [Website Link Placeholder]
+
+**Paper:** [https://microsoft.github.io/trellis.2](https://microsoft.github.io/trellis.2)
+
+**Repository:** [https://github.com/microsoft/TRELLIS.2](https://github.com/microsoft/TRELLIS.2)
+
+**Project Page:** [https://microsoft.github.io/trellis.2](https://microsoft.github.io/trellis.2)
 
 ## Introduction
 
@@ -19,6 +22,15 @@ language:
 
 Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate **arbitrary 3D assets** with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency.
 
+## Model Details
+
+*   **Developed by:** Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, Jiaolong Yang
+*   **Model Type:** Flow-Matching Transformers with Sparse Voxel based 3D VAE
+*   **Parameters:** 4 Billion
+*   **Input:** Single Image
+*   **Output:** 3D Asset (Mesh with PBR Materials)
+*   **Resolution:** Varies from 512³ to 1536³ (Voxel Grid Resolution)
+
 ## Key Features
 
 *   **O-Voxel Representation:** An omni-voxel structure that encodes both geometry and appearance. It supports:
@@ -26,8 +38,9 @@ Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes)
     *   **Rich Appearance:** Captures PBR attributes (including opacity for translucent surfaces) aligned with geometry.
     *   **Efficiency:** Instant optimization-free bidirectional conversion between meshes and O-Voxels (ms to seconds).
 *   **High-Resolution Generation:** The model is trained to generate fully textured assets at **up to 1536³ resolution**.
-*   **High-Fidelity while Compact Latent Space:** Utilizes a Sparse 3D VAE with **16× spatial downsampling**, encoding a $1024^3$ asset into only ~9.6K latent tokens with negligible perceptual degradation.
-*   **State-of-the-Art Speed:** Inference is significantly faster than existing large 3D models.
+*   **High-Fidelity while Compact Latent Space:** Utilizes a Sparse 3D VAE with **16× spatial downsampling**, encoding a 1024³ asset into only ~9.6K latent tokens with negligible perceptual degradation.
+*   **Shape-conditioned Texture Generation:** Generates textures for input 3D meshes and reference images.
+*   **State-of-the-Art Speed:** Inference is highly efficient; see table below.
 
 ## Inference Speed (NVIDIA H100 GPU)
 
@@ -39,25 +52,19 @@ Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes)
 
 ## Known Limitations
 
-*   **Geometric Artifacts (Small Holes):** While O-Voxels handle complex topology well, the generated raw meshes may occasionally contain small holes or minor topological discontinuities. For applications requiring strictly watertight geometry (e.g., 3D printing), standard mesh post-processing steps (such as hole-filling algorithms) may be necessary.
-*   **Base Model Status (No Alignment):** TRELLIS.2-4B is a pre-trained foundation model. It has **not** been aligned with human preferences (e.g., via RLHF) or fine-tuned for specific aesthetic standards. Consequently, the outputs reflect the distribution of the training data and may vary in style; users may need to experiment with inputs to achieve the desired artistic result.
-
-## Model Details
+*   **Geometric Artifacts (Small Holes):** While O-Voxels handle complex topology well, the generated raw meshes may occasionally contain small holes or minor topological discontinuities. For applications requiring strictly watertight geometry (e.g., 3D printing), we provide accompanying mesh post-processing scripts, such as hole-filling algorithms.
+*   **Base Model w/o Alignment:** TRELLIS.2-4B is a pre-trained foundation model. It has **not** been aligned with human preferences (e.g., via RLHF) or fine-tuned for specific aesthetic standards. Consequently, the outputs reflect the distribution of the training data and may vary in style; users may need to experiment with inputs to achieve the desired artistic result.
 
-*   **Developed by:** Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, Jiaolong Yang
-*   **Model Type:** Flow-Matching Transformers with Sparse Voxel based 3D VAE
-*   **Parameters:** 4 Billion
-*   **Input:** Single Image
-*   **Output:** 3D Asset (Mesh with PBR Materials)
-*   **Resolution:** Varies from 512³ to 1536³ (Voxel Grid Resolution)
+We are actively working on improving the model and addressing these limitations.
 
 ## Usage
 
-*Note: Please refer to the official [GitHub Repository] for installation instructions and dependencies.*
+*Note: Please refer to the official [GitHub Repository](https://github.com/microsoft/TRELLIS.2) for installation instructions and dependencies.*
 
 ```python
 import os
 os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"  # Can save GPU memory
 import cv2
 import imageio
 from PIL import Image
@@ -67,26 +74,26 @@ from trellis2.utils import render_utils
 from trellis2.renderers import EnvMap
 import o_voxel
 
+# 1. Setup Environment Map
 envmap = EnvMap(torch.tensor(
     cv2.cvtColor(cv2.imread('assets/hdri/forest.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB),
     dtype=torch.float32, device='cuda'
 ))
 
-# Load a pipeline from a model folder or a Hugging Face model hub.
+# 2. Load Pipeline
 pipeline = Trellis2ImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS.2-4B")
 pipeline.cuda()
 
-# Load an image
+# 3. Load Image & Run
 image = Image.open("assets/example_image/T.png")
-
-# Run the pipeline
 mesh = pipeline.run(image)[0]
+mesh.simplify(16777216) # nvdiffrast limit
 
-# Render the outputs
+# 4. Render Video
 video = render_utils.make_pbr_vis_frames(render_utils.render_video(mesh, envmap=envmap))
 imageio.mimsave("sample.mp4", video, fps=15)
 
-# GLB files can be extracted from the outputs
+# 5. Export to GLB
 glb = o_voxel.postprocess.to_glb(
     vertices            =   mesh.vertices,
     faces               =   mesh.faces,
@@ -95,10 +102,12 @@ glb = o_voxel.postprocess.to_glb(
     attr_layout         =   mesh.layout,
     voxel_size          =   mesh.voxel_size,
     aabb                =   [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
-    decimation_target   =   100000,
+    decimation_target   =   1000000,
     texture_size        =   2048,
+    remesh              =   False,
+    verbose             =   True
 )
-glb.export("sample.glb")
+glb.export("sample.glb", include_normals=False)
 ```
 
 ## Citation
@@ -106,7 +115,13 @@ glb.export("sample.glb")
 If you find this model useful for your research, please cite our work:
 
 ```
-TBD
+@article{
+    xiang2025trellis2,
+    title={Native and Compact Structured Latents for 3D Generation},
+    author={Xiang, Jianfeng and Chen, Xiaoxue and Xu, Sicheng and Wang, Ruicheng and Lv, Zelong and Deng, Yu and Zhu, Hongyuan and Dong, Yue and Zhao, Hao and Yuan, Nicholas Jing and Yang, Jiaolong},
+    journal={Tech report},
+    year={2025}
+}
 ```
 
 ## License