Add comprehensive dataset card for EPiC_Data

README.md

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+---
+task_categories:
+- image-to-video
+---
+
+# EPiC_Data: Training Data for Efficient Video Camera Control Learning
+
+This repository contains the training data for **EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance**. EPiC is an efficient and precise camera control learning framework that automatically constructs high-quality anchor videos without expensive camera trajectory annotations. This dataset provides the approximately 5K training videos and related components used to train the EPiC framework for image-to-video (I2V) and video-to-video (V2V) camera control tasks.
+
+**Paper:** [EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance](https://huggingface.co/papers/2505.21876)
+**Project Page:** [https://zunwang1.github.io/Epic](https://zunwang1.github.io/Epic)
+**Code:** [https://github.com/wz0919/EPiC](https://github.com/wz0919/EPiC)
+
+## Paper Abstract
+
+Recent approaches on 3D camera control in video diffusion models (VDMs) often
+create anchor videos to guide diffusion models as a structured prior by
+rendering from estimated point clouds following annotated camera trajectories.
+However, errors inherent in point cloud estimation often lead to inaccurate
+anchor videos. Moreover, the requirement for extensive camera trajectory
+annotations further increases resource demands. To address these limitations,
+we introduce EPiC, an efficient and precise camera control learning framework
+that automatically constructs high-quality anchor videos without expensive
+camera trajectory annotations. Concretely, we create highly precise anchor
+videos for training by masking source videos based on first-frame visibility.
+This approach ensures high alignment, eliminates the need for camera trajectory
+annotations, and thus can be readily applied to any in-the-wild video to
+generate image-to-video (I2V) training pairs. Furthermore, we introduce
+Anchor-ControlNet, a lightweight conditioning module that integrates anchor
+video guidance in visible regions to pretrained VDMs, with less than 1% of
+backbone model parameters. By combining the proposed anchor video data and
+ControlNet module, EPiC achieves efficient training with substantially fewer
+parameters, training steps, and less data, without requiring modifications to
+the diffusion model backbone typically needed to mitigate rendering
+misalignments. Although being trained on masking-based anchor videos, our
+method generalizes robustly to anchor videos made with point clouds during
+inference, enabling precise 3D-informed camera control. EPiC achieves SOTA
+performance on RealEstate10K and MiraData for I2V camera control task,
+demonstrating precise and robust camera control ability both quantitatively and
+qualitatively. Notably, EPiC also exhibits strong zero-shot generalization to
+video-to-video scenarios.
+
+## Sample Usage
+
+To download the EPiC training dataset, which consists of approximately 5,000 training videos, navigate to the `data/train` directory within your EPiC code repository clone, and use the following `wget` and `unzip` commands:
+
+```bash
+# Assuming you are in the root directory of the EPiC code repository, navigate to data/train
+cd data/train
+
+# Download the main training videos
+wget https://huggingface.co/datasets/ZunWang/EPiC_Data/resolve/main/train.zip
+unzip train.zip
+```
+
+**(Optional)** You can also download the pre-extracted VAE latents, which can save several hours of preprocessing time:
+
+```bash
+wget https://huggingface.co/datasets/ZunWang/EPiC_Data/resolve/main/train_joint_latents.zip
+unzip train_joint_latents.zip
+```
+
+After downloading and unzipping, your `data/train` folder, following preprocessing steps (as described in the [EPiC GitHub repository](https://github.com/wz0919/EPiC)), should have a structure similar to this:
+
+```
+data/
+└── train/
+    ├── caption_embs/
+    ├── captions/
+    ├── joint_latents/
+    ├── masked_videos/
+    ├── masks/
+    └── videos/
+```
+
+For detailed instructions on preprocessing this data (e.g., extracting caption embeddings or VAE latents) and training the EPiC model, please refer to the [EPiC GitHub repository](https://github.com/wz0919/EPiC).