Add model card for ReImagine

README.md

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+---
+license: apache-2.0
+pipeline_tag: image-to-video
+---
+
+# ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis
+
+[**Project Page**](https://keruzheng.github.io/ReImagine-Project/) | [**Paper (arXiv)**](https://arxiv.org/abs/2604.19720) | [**Code**](https://github.com/Taited/ReImagine) | [**Demo**](https://taited-reimagine.hf.space/)
+
+**ReImagine** is a framework for controllable high-quality human video generation. It revisits the problem from an image-first perspective, where high-quality human appearance is learned via image generation and used as a prior for video synthesis. This approach decouples appearance modeling from temporal consistency.
+
+The system utilizes a pose- and viewpoint-controllable pipeline that combines a pretrained image backbone with SMPL-X-based motion guidance, followed by a training-free temporal refinement stage based on a pretrained video diffusion model.
+
+## Getting Started
+
+### Installation
+
+```bash
+conda create -n reimagine python=3.10
+conda activate reimagine
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
+pip install -e .
+```
+
+### Pretrained Weights
+
+ReImagine utilizes base models and specific LoRA weights. You can download the weights using the Hugging Face CLI:
+
+```bash
+# Download base FLUX.1 model
+hf download black-forest-labs/FLUX.1-Kontext-dev \
+  --local-dir ./models/FLUX.1-Kontext-dev \
+  --exclude "flux1-kontext-dev.safetensors" \
+  --exclude "vae/**"
+
+# Download ControlNet
+hf download jasperai/Flux.1-dev-Controlnet-Surface-Normals \
+  --local-dir ./models/Flux.1-dev-Controlnet-Surface-Normals
+
+# Download ReImagine LoRA Weights
+hf download taited/ReImagine-Pretrained --local-dir ./models/ReImagine-Pretrained
+```
+
+## Inference
+
+To perform image-first synthesis, use the provided inference script:
+
+```bash
+python inference_img.py
+```
+This script requires a wide reference image (front and back views) and a normal map generated from SMPL-X. For video synthesis, the temporal-refinement stage is used to ensure consistency across frames.
+
+## Citation
+
+If you find this project useful, please consider citing the paper:
+
+```bibtex
+@article{sun2025rethinking,
+  title={ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis},
+  author={Sun, Zhengwentai and Zheng, Keru and Li, Chenghong and Liao, Hongjie and Yang, Xihe and Li, Heyuan and Zhi, Yihao and Ning, Shuliang and Cui, Shuguang and Han, Xiaoguang},
+  journal={arXiv preprint arXiv:2604.19720},
+  year={2026},
+  url={https://arxiv.org/abs/2604.19720v1}
+}
+```