Add model card and metadata

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+---
+
+# AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting
+
+[**Paper**](https://huggingface.co/papers/2605.10239) | [**Code**](https://github.com/xmw666/AdaptSplat)
+
+AdaptSplat is a lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). It introduces a single Frequency-Preserving Adapter (FPA) of only 1.5M parameters into a generic architecture to achieve superior performance in cross-domain generalization and high-frequency geometric fidelity. By extracting direction-aware high-frequency structural priors from a vision foundation model backbone (DINOv3-distilled ConvNeXt), it effectively compensates for high-frequency attenuation caused by over-smoothing in deep features.
+
+## Inference
+
+To run inference, please refer to the environment setup in the [official repository](https://github.com/xmw666/AdaptSplat). Once the environment and weights are prepared, you can run inference using the following commands:
+
+### Single-GPU Inference
+```bash
+CUDA_VISIBLE_DEVICES=0 python inference.py --config configs/inference.yaml
+```
+
+### Multi-GPU (DDP) Inference
+```bash
+torchrun --nproc_per_node=8 inference_ddp.py --config configs/inference.yaml
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{adaptsplat2026,
+  title={AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting},
+  author={Mingwei Xing, Xinliang Wang, Yifeng Shi},
+  journal={arXiv preprint arXiv:2605.10239},
+  year={2026}
+}
+```