Add model card

Hi! I'm Niels, part of the community team at Hugging Face. This PR adds a comprehensive model card for the artifacts related to the paper [Memorization in 3D Shape Generation: An Empirical Study](https://huggingface.co/papers/2512.23628).

The model card includes:
- Metadata such as the `pipeline_tag` and `license`.
- Links to the paper, project page, and code repository.
- A brief description of the evaluation framework and the models.
- Sample usage commands for text, class, and image conditional generation.
- Citation information.

README.md

@@ -0,0 +1,61 @@
+---
+license: apache-2.0
+pipeline_tag: text-to-3d
+---
+
+# Memorization in 3D Shape Generation
+
+This repository contains the official implementation of the paper [Memorization in 3D Shape Generation: An Empirical Study](https://huggingface.co/papers/2512.23628).
+
+[**Project Page**](https://urrealhero.github.io/3DGenMemorizationWeb/) | [**GitHub Repository**](https://github.com/zlab-princeton/3d_mem)
+
+## Description
+Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. This work introduces an evaluation framework to quantify memorization ($Z_U$) in 3D generative models and studies the influence of different data and modeling designs on memorization. Through controlled experiments with a latent vector-set (Vecset) diffusion model, the authors provide analysis and strategies to reduce memorization without degrading generation quality.
+
+## Usage
+
+For environment setup and instructions on data curation, please refer to the [official GitHub repository](https://github.com/zlab-princeton/3d_mem).
+
+### Inference
+
+**Text-conditional generation**
+```bash
+python inference.py \
+    --config configs/Baseline.yaml \
+    --ckpt PATH/TO/CHECKPOINT \
+    --out_dir outputs_text/ \
+    --num_samples 4 \
+    --text "a chair"
+```
+
+**Class-conditional generation**
+```bash
+python inference.py \
+    --config configs/Conditioning/LVIS-16-Category.yaml \
+    --ckpt PATH/TO/CHECKPOINT \
+    --out_dir outputs_lvis/ \
+    --num_samples 4 \
+    --class_id 0
+```
+
+**Image-conditional generation**
+```bash
+python inference.py \
+    --config configs/Conditioning/Image.yaml \
+    --ckpt PATH/TO/CHECKPOINT \
+    --out_dir outputs_image \
+    --num_samples 1 \
+    --image_path data_sprite.png \
+    --image_views 12 \
+    --image_pick random
+```
+
+## Citation
+```bibtex
+@article{pu2025memorization,
+  title={Memorization in 3D Shape Generation: An Empirical Study},
+  author={Pu, Shu and Zeng, Boya and Zhou, Kaichen and Wang, Mengyu and Liu, Zhuang},
+  journal={arXiv preprint arXiv:2512.23628},
+  year={2025}
+}
+```