Create README.md

README.md

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+---
+language:
+- en
+tags:
+- webdataset
+- audio
+- motion
+---
+
+## UniLS-Talk Dataset
+
+To enable research on unified speaking and listening avatar generation, we curate and construct the **UniLS-Talk Dataset**, a large-scale collection of high-quality 3D facial motion data. We apply a carefully designed tracking pipeline to extract per-frame [FLAME](https://flame.is.tue.mpg.de/) parameters, including expression coefficients, eye-gaze, jaw pose and head pose annotations. The dataset comprises two complementary parts:
+
+- **Paired conversational data** sourced from the [Seamless Interaction](https://ai.meta.com/research/seamless-interaction/) dataset, providing synchronized dual-speaker videos with natural turn-taking dynamics between speaking and listening.
+- **Unpaired multi-scenario data** aggregated from CelebV, TalkingHead-1KH, TEDTalk, VFHQ, and other in-the-wild videos, covering diverse facial behaviors across identities and environments (news broadcasts, interviews, casual talking, etc.).
+
+| Category | Source | Hours | Audio | Motion |
+|----------|--------|-------|-------|--------|
+| **Paired Conversational** | Seamless Interaction Dataset | 657.5 h | ✅ | ✅ |
+| **Unpaired Multi-Scenario** | Diverse identities and environments from in-the-wild videos | **546.5 h** | ❌ | ✅ |
+| **Total** | | **1,204 h** | | |
+
+The paired conversational data is split into **622.5 hours** for training, **4.8 hours** for validation, and **30.2 hours** for testing. All data includes FLAME expression parameters, jaw and head pose, and eye-gaze annotations at 25 fps.