Hunyuan-GameCraft
Hunyuan-GameCraft is a research-grade diffusion-based framework for high-dynamic, controllable interactive game video generation that unifies keyboard and mouse inputs into a continuous camera representation and uses hybrid history-conditioned training to produce long, consistent gameplay videos.
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Quick Overview
Best for: Software & Gaming
What it does
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Best fit
Software & Gaming
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Hunyuan-GameCraft is a diffusion-based framework designed to generate high-dynamic, temporally coherent interactive gameplay videos. It unifies standard keyboard and mouse inputs into a shared continuous camera representation, encodes actions with a light-weight action encoder, and uses a hybrid history-conditioned training strategy to autoregressively extend video sequences while preserving scene information. The framework incorporates model distillation to improve inference efficiency and is trained on a large-scale dataset of over one million gameplay recordings across more than 100 AAA games, then fine-tuned on annotated synthetic data to improve precision and control.
An open-source model by Tencent that generates playable, high-dynamic game videos from a single image and user actions, offering fine-grained control and impressive realism.
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Unified input representation
Standard keyboard and mouse inputs are transformed into a shared continuous camera representation space to enable smooth interpolation between camera and movement operations.
Light-weight action encoder
A compact action encoder encodes input camera trajectories and adds action and image features after patchify for conditioned generation.
Hybrid history-conditioned training
A hybrid history-conditioned strategy extends video sequences autoregressively and preserves game scene information using a variable mask indicator for history and predicted frames.
Model distillation for efficiency
Model distillation reduces computational overhead while maintaining long-term temporal consistency and playability for real-time deployment.
Large-scale game dataset
Trained on over one million gameplay recordings spanning 100+ AAA games and fine-tuned on an annotated synthetic dataset to enhance visual fidelity and control precision.
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Interactive gameplay video generation
Generate controllable, temporally coherent gameplay videos from reference images, prompts, and input action signals for interactive experiences.
Third-person and first-person game scenarios
Generalizes to both third-person and first-person perspectives for a variety of game genres and camera setups.
Trajectory exploration and visualization
Explore and visualize multiple complex camera and movement trajectories for single scenes to evaluate continuity and consistency.
Real-time deployment in interactive environments
Distilled models and efficiency optimizations support deployment in real-time or near-real-time game scenarios.
Integrations
Hugging Face
Project code and model artifacts are referenced via 'Paper Code 🤗 Hugging Face' on the project page.
Benefits
Limitations
No verified limitations are available.
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Read the project paper on arXiv (https://arxiv.org/abs/2506.17201).
- 2 Step 2: Access the project code and assets via the provided 'Paper Code 🤗 Hugging Face' link on the project page.
- 3 Step 3: Contact the project team for collaboration or internship opportunities ([email protected]).
Support
Primary contact for collaboration and internships: [email protected]
paper
Project paper and method details: https://arxiv.org/abs/2506.17201
code
Project code and assets available via Hugging Face link indicated on the project page ('Paper Code 🤗 Hugging Face').
API
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