Update README.md

README.md

@@ -1,3 +1,60 @@
 ---
+pipeline_tag: video-to-video
 license: apache-2.0
+language:
+- en
+base_model:
+- Wan-AI/Wan2.1-T2V-1.3B
 ---
+
+# DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
+
+We propose DecMem, a decoupled memory architecture that employs Sparse Global Memory for efficient fine-grained access to global history and Anchored Local Memory for stable and high-quality extrapolation. 
+
+[**Project Page**](https://jeffreyyzh.github.io/DecMem-Page/) | [**Paper**](https://arxiv.org/abs/2605.31336) | [**Code**](https://github.com/KlingAIResearch/DecMem)
+
+## Checkpoints
+
+Download the Wan2.1 backbone (VAE + tokenizer weights used by the pipeline):
+
+```bash
+huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B \
+    --local-dir-use-symlinks False \
+    --local-dir wan_models/Wan2.1-T2V-1.3B
+```
+
+Download DecMem trained checkpoints from HuggingFace:
+
+```bash
+huggingface-cli download KlingTeam/DecMem --local-dir checkpoints
+```
+
+Checkpoint layout expected by training / inference scripts:
+
+```
+checkpoints/
+└── decmem.pt             # released weights
+```
+
+## Quick start
+
+We provide the example video-pose pairs for quick inference. The inference is Block-by-block causal denoising manner with KV cache.
+
+```bash
+bash scripts/infer_example.sh
+```
+
+## Citation
+If you find our work helpful, please cite our paper:
+
+```bibtex
+@misc{yang2026decmemminutelongconsistentworld,
+      title={DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory}, 
+      author={Zhenhao Yang and Xiaoshi Wu and Zhengyao Lv and Xiaoyu Shi and Xintao Wang and Pengfei Wan and Kun Gai and Kwan-Yee K. Wong},
+      year={2026},
+      eprint={2605.31336},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.31336}, 
+}
+```