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XGEN-JING: An Egocentric
Interactive Experience Model

XGEN Team

We present XGEN-JING, an egocentric interactive experience model built on MiniMax-H3. Given actions, reference images, and observation history, JING generates first-person video and audio for navigation, object interaction, and conversation.

• Camera control. Explore everyday places and imagined worlds through keyboard-controlled movement.
• Interaction and dialogue. Guide object interactions and character conversations with text, with video and audio generated together.
• Reference conditioning. Combine character, object, and scene images to compose an experience and explore different actions from the same starting point.

This release provides four-step bidirectional inference, example cases, and Prompt skills. The causal model and technical report are coming soon.

📋 Release Plan

• JING-Flash-v1 — Four-step bidirectional model.
• Inference code and examples — Camera controls, reference images, and joint audio/video generation.
• Prompt skills — Generate validated inference cases from stories and reference images.
• Causal model — Coming soon.
• Technical report — Coming soon.

🚀 Quick Start

1. Installation

Use Python 3.12 and a compatible CUDA environment. The demo has been validated on six H100 GPUs: one for the text encoder, one for the video/audio VAEs, and four for the DiT with sequence parallelism. FlashAttention-4 is the default backend.

git clone https://github.com/XGEN-Labs/XGEN-JING.git
cd XGEN-JING
python3 -m pip install -r requirements.txt

2. Model weights

JING Flash is built upon MiniMax-H3 Ref2VA and FlashGen for a faster experience.

The demo loads the JING-Flash-v1 transformer from Hugging Face. The text encoder, tokenizer, processor, video/audio VAEs, and schedulers come from Diffusers-format MiniMax-H3.

Required model files are downloaded automatically on first use and reused from the Hugging Face cache. Set HF_HOME to choose the cache location. No manual download or weight directory is required. Repository IDs are configured in configs/base.yaml.

3. Inference

python3 demo_bidirection.py check_config=true
bash demo.sh cases=examples/bakery_greeting.json

Results are saved to examples/outputs/.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 bash demo.sh \
  model.h3=/path/to/MiniMax-H3 \
  model.transformer=/path/to/XGEN-JING \
  output.directory=/path/to/outputs

{
  "prompt": "First-person view: approach the counter and greet the baker.",
  "repeat": 3,
  "control": ["w", "w,a", ""]
}

Prompt skills

Use Prompt skills to turn a story and reference images into a validated cases JSON file, ready to pass to demo.sh. It includes a standalone guide and supports a configurable Chat Completions API.

🤝 Acknowledgments

We thank the MiniMax-H3 team for opening their audio/video foundation model, and the FlashGen team for their four-step acceleration work and model release.

Our inference implementation builds on the open-source infrastructure provided by Diffusers, SGLang, and FlashAttention. See NOTICE for component attributions.

We also thank the WBench team for their open-source benchmark and evaluation tools for interactive video world models.

License

XGEN-JING code and model weights are released under the MiniMax H3 Community License Agreement. Third-party components retain their original licenses; see NOTICE.