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README.md

@@ -1,4 +1,7 @@
-# Scene-Decoupled Video Dataset
+# CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation 
+CVPR 2026: [Arxiv](https://arxiv.org/pdf/2602.06959) | [Project Page](https://karine-huang.github.io/CineScene/) 
+
+## Scene-Decoupled Video Dataset
 
 TL;DR: The Scene-Decoupled Video Dataset, introduced in CineScene, is a large-scale synthetic dataset for **video generation with decoupled scene**, which encompasses diverse scenes, subjects, and camera movements. This dataset contains camera trajectories, equirectangular panorama (scene image), and videos with/without dynamic subject. The data is organized into "With Human" (whuman) and "Without Human" (wohuman) categories, while panoramas are scene-decoupled and shared across both.
 
@@ -52,10 +55,10 @@ TL;DR: The Scene-Decoupled Video Dataset, introduced in CineScene, is a large-sc
 
 ## 3. Dataset Construction
 
-We follow the asset collection pipeline established by **RecamMaster**, but introduce three significant enhancements to support more complex generative tasks:
+We follow the asset collection pipeline established by [**RecamMaster**](https://arxiv.org/pdf/2503.11647), but introduce three significant enhancements to support more complex generative tasks:
 
 1.  **Decoupled Scenes**: We provide static 360° panoramic images (Equirectangular) for every scene. This allows for explicit background conditioning and facilitates novel view synthesis from any angle.
-2.  **Extended Camera Range**: Our dataset covers significantly larger view changes (approx. **75°**) compared to the 5–60° range provided in previous datasets [1].
+2.  **Extended Camera Range**: Our dataset covers significantly larger view changes (approx. **75°**) compared to the 5–60° range provided in [previous datasets](https://huggingface.co/datasets/KlingTeam/MultiCamVideo-Dataset).
 3.  **Paired Subject/Background Data**: Every scene includes both "with-subject" (*whuman*) and "background-only" (*wohuman*) video sequences. This paired data is ideal for training models on subject-background decoupling, motion transfer, and cinematic composition.
 
 ## 4. useful script
@@ -65,7 +68,7 @@ We follow the asset collection pipeline established by **RecamMaster**, but intr
 ```bash
 sudo apt-get install git-lfs
 git lfs install
-git clone https://huggingface.co/datasets/KwaiVGI/Scene-Decoupled-Video-Dataset
+git clone https://huggingface.co/datasets/KlingTeam/Scene-Decoupled-Video-Dataset
 cat Scene-Decoupled-Video-Dataset.part* > Scene-Decoupled-Video-Dataset.tar.gz
 tar -xvf Scene-Decoupled-Video-Dataset.tar.gz
 ```
@@ -82,6 +85,3 @@ To extract perspective frames from the panoramic images:
   ```
 
   
-
-References
-[1] Bai J, Xia M, Fu X, et al. Recammaster: Camera-controlled generative rendering from a single video[J]. arXiv preprint arXiv:2503.11647, 2025.