Hunyuan-GameCraft: Real-Time Game Video Synthesis

Updated 1 July 2025

Hunyuan-GameCraft is a diffusion-based framework that converts user inputs into extended, high-fidelity gameplay videos.
It employs unified action representation, hybrid history conditioning, and model distillation for smooth, real-time video synthesis.
Empirical results demonstrate improved temporal consistency, action control accuracy, and accelerated inference suitable for dynamic gaming environments.

Hunyuan-GameCraft is a diffusion-based, high-dynamic interactive video generation framework specifically developed for real-time game environments. It is engineered to translate standard keyboard and mouse actions into temporally and visually coherent gameplay videos, supporting extended video sequences, fine-grained action controllability, and efficient, real-time inference. Addressing the challenges of temporal consistency, dynamics, control fidelity, and computational efficiency in interactive video synthesis, Hunyuan-GameCraft combines unified action-space modeling, hybrid history-conditioned training, and model distillation—operating atop a foundation of large-scale AAA game video pretraining and synthetic high-precision interaction fine-tuning.

1. System Architecture and Design Principles

Hunyuan-GameCraft is structured around a controllable video diffusion backbone (adapted from MM-DiT-based HunyuanVideo) and introduces architectural components enabling dynamic, interactive video generation:

Inputs: Supports a reference image (initial visual seed), a text prompt (semantic scene control), and a sequence of discrete user actions (keyboard/mouse).
Unified Action Representation: Keyboard and mouse inputs are mapped into a continuous camera representation space $\mathcal{A}$ , enabling smooth interpolation of camera translation and rotation and unification of diverse control modalities.
Action Feature Injection: An action encoder processes camera trajectories, generating token representations that are integrated into the video backbone via token addition after patchification—this preserves spatial coherence while allowing prompt-adaptive temporal transitions.
Hybrid History Conditioning: Mask-based autoregressive video extension leverages both head (history) and chunk (prediction) latent regions, enabling the model to preserve global scene memory while synthesizing new frames conditioned on user actions.
Phased Consistency Model Distillation: Knowledge distillation compresses the full iterative diffusion process into a compact student network, reducing the inference gap from hundreds of diffusion iterations down to as few as 8, facilitating real-time response.

The confluence of these strategies allows the system to respond rapidly to complex input streams while maintaining cinematic smoothness, scene fidelity, and action accuracy.

2. Unified Action Control Mechanism

Hunyuan-GameCraft adopts a camera-centric parameterization for all user actions:

$\mathcal{A} = \left\{ \mathbf{a} = \left(\mathbf{d}_{\text{trans}}, \mathbf{d}_{\text{rot}}, \alpha, \beta \right) \;\middle|\; \begin{matrix} \mathbf{d}_{\text{trans}}, \mathbf{d}_{\text{rot}} \in \mathbb{S}^2 \ \alpha \in [0, v_{\max}]\ \beta \in [0, \omega_{\max}] \end{matrix} \right\}$

$\mathbf{d}_{\text{trans}}, \mathbf{d}_{\text{rot}}$ denote unit translation/rotation directions on the sphere.
$\alpha$ and $\beta$ encode the respective translation and rotation speeds.

An encoder module (lightweight CNN with pooling) translates these parameters into Plücker-style camera embeddings, which are then incorporated as additional tokens to the model. This approach supports fine-grained, physically plausible, and smoothly blended camera actions, enabling both discrete step inputs and continuous control.

3. Hybrid History-Conditioned Autoregressive Video Generation

To address the challenge of long-term temporal consistency in scene generation, the hybrid history-conditioned strategy splits the latent space into head (history) and chunk (future prediction) regions. A variable mask indicator distinguishes between the two:

Head Latents (mask=1): Encapsulate previous generation context, not denoised, serving as temporal anchors.
Chunk Latents (mask=0): Regions to be predicted/denoised based on current action input and context.

During training and inference, this design allows the model to produce temporally extended videos by iteratively sliding the history window and conditioning on prior scene context and new action signals. Flow matching is utilized to ensure smooth transitions and scene memory retention. This strategy supports both one-shot image-to-video and incremental video extension within a unified model paradigm, mitigating error accumulation and promoting long-horizon video stability.

4. Accelerated Inference through Model Distillation

The interactive requirements of gameplay necessitate rapid inference. Hunyuan-GameCraft applies PCM (Phased Consistency Model) distillation, where a student model is trained to emulate the classifier-guided outputs of the teacher diffusion process:

$L_{\text{cfg}} = \mathbb{E}_{w \sim p_w, t \sim U[0,1]} \left[ \left\| \hat{u}_\theta(z_t, t, w, T_s) - u_\theta^s(z_t, t, w, T_s) \right\|^2_2 \right]$

$\hat{u}_\theta(z_t, t, w, T_s) = (1+w)u(z_t, t, T_s) - w u_\theta(z_t, t, \emptyset)$

As a result, the distilled network achieves inference speeds of $\sim$ 6.6 FPS in real-time scenarios, with video quality (e.g., FVD, image quality, temporal consistency) maintained to within the margin of the original multi-step diffusion. This enables the deployment of the model in environments with substantial latency and throughput constraints, such as playtesting tools or in-game content engines.

5. Dataset Design and Training Regimen

Hunyuan-GameCraft is pretrained on a dataset comprising over 1 million annotated gameplay video clips (~6 seconds each, 1080p) from more than 100 AAA games, encompassing a broad range of scenes, mechanics, and styles. Data curation involves:

Scene and Action-aware Segmentation: Automated tools (PySceneDetect, RAFT) promote meaningful clip boundaries.
Quality and Diversity Filtering: Luminance thresholds, video quality models, and VLM-based semantic diversity controls.
Interaction Annotation: Dense framewise 6-DoF camera poses via structure-from-motion pipelines (e.g., Monst3R).
Hierarchical Captioning: Scene- and action-specific textual descriptions for prompt conditioning.

Fine-tuning leverages a curated synthetic dataset of 3,000 high-precision 3D asset-driven clips, covering diverse trajectories and camera speeds, to strengthen motion precision and controllability. Data mixing (e.g., synthetic:real at 1:5) balances action fidelity against visual and dynamic realism.

6. Empirical Performance and Evaluation

Hunyuan-GameCraft demonstrates state-of-the-art performance compared to prior interactive game video generators:

Model	FVD↓	ImgQ↑	Dynamic↑	Aesthetic↑	Temp. Consist.↑	RPE (Trans/Rot)↓	FPS↑
Matrix-Game	2260.7	0.72	31.7	0.65	0.94	0.18 / 0.35	0.06
Hunyuan-GameCraft	1554.2	0.69	67.2	0.67	0.95	0.08 / 0.20	0.25
+ PCM (Distilled)	1883.3	0.67	43.8	0.65	0.93	0.08 / 0.20	6.6

Action Control Accuracy: RPE (Relative Pose Error) for translation/rotation is minimized (0.08/0.20, Sim3 Umeyama alignment), reflecting high fidelity in camera-action response.
Temporal Consistency/Dynamics: Quantitative and user paper results confirm superior video continuity under dynamic conditions and diverse inputs.
User Study Outcome: Ranked highest in subjective assessments of video quality, action alignment, smoothness, and cinematic realism.

Ablation studies indicate that: (i) hybrid history conditioning substantially improves long-sequence coherence and responsiveness, (ii) the token addition mechanism for control fissure is both efficient and high-performing, and (iii) inclusion of synthetic data is pivotal for precise action rendering, while real data is critical for artistic fidelity and stochastic diversity.

7. Implementation and Deployment Considerations

Hunyuan-GameCraft is architected to balance high realism with practical deployment constraints:

Token Addition for Action Injection: Offers computationally lightweight feature fusion without architectural overhead.
Phased Consistency Model Distillation: Reduces latency, memory, and compute, supporting deployment on real-time inference servers or edge devices.
Balanced Data Design: Stratified sampling, inversion, and augmentations prevent bias towards generic forward motion and bolster generalization to a range of scene types and action profiles.
Extensibility: Hybrid history conditioning enables a seamless transition between image-to-video, video extension, and full autoregressive scene synthesis within a single pipeline.

This design supports a range of production and prototyping applications, including scene previewing, playable cutscene rendering, rapid content prototyping, and interactive creative engines for both designers and end users.

Hunyuan-GameCraft constitutes a comprehensive framework for interactive game video generation, integrating unified action-space modeling, hybrid autoregressive conditioning, and efficient distillation. Utilizing large-scale real and synthetic data, it enables real-time, high-fidelity, and highly controllable cinematic scene synthesis in modern gaming contexts.

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