Hunyuan-GameCraft

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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#163 in Video Generation (163 tools)
Added 1 year ago
Data reviewed Jul 15, 2026

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Quick Overview

Best for: Software & Gaming

What it does

AI software for decision-makers comparing workflow fit and alternatives.

Best fit

Software & Gaming

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Hunyuan-GameCraft

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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Key Features

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

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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 πŸ€— Hugging Face' on the project page.

Benefits

Fine-grained action control via unified keyboard/mouse to camera representation.
Improved long-term consistency and history preservation using hybrid history-conditioned training.
Reduced inference cost and improved playability through model distillation.
Broad generalization from training on a large-scale dataset of >1M gameplay recordings across 100+ AAA games.
Enhanced visual fidelity and control precision via fine-tuning on annotated synthetic data.

Limitations

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Frequently Asked Questions

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Getting Started

  1. 1 Step 1: Read the project paper on arXiv (https://arxiv.org/abs/2506.17201).
  2. 2 Step 2: Access the project code and assets via the provided 'Paper Code πŸ€— Hugging Face' link on the project page.
  3. 3 Step 3: Contact the project team for collaboration or internship opportunities ([email protected]).

Support

email

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 πŸ€— Hugging Face').

API

Available: No

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