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GameGen-X: Interactive Open-world Game Video Generation

Haoxuan Che, Xuanhua He, Quande Liu, Cheng Jin, Hao Chen

arXiv:2411.00769v3cs.CVcs.AI

TL;DR

Complex open-world game content remains difficult to generate and control interactively. GameGen-X combines a diffusion transformer with OGameData and a two-stage training approach; the paper reports strong generation quality and control over environments and characters. The authors describe it as a potential scalable, efficient auxiliary tool for traditional game design.

  • Problem

    Prior neural game simulators mainly focused on 2D or early 3D games, while generating complex open-world content and responding meaningfully to user inputs remain challenges.

    Source: §1 · p. 2

  • Method

    GameGen-X uses a foundation model trained for text-to-video generation and video continuation, then adds InstructNet for multimodal interactive control, using OGameData.

    Source: §1 · pp. 2–3, §3.2 · p. 6

  • Results

    The authors report that GameGen-X excels in high-quality game-content generation and offers superior control over environments and characters.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The authors present GameGen-X as a potential scalable and efficient auxiliary tool for traditional game design methods.

    Source: §1 · p. 3

    The current dataset distribution limits the model's ability to generate and manage interactions among multiple characters, especially coordinated combat or cooperative tasks.

    Source: §E.1 · p. 46

Abstract

from arXiv · show

We introduce GameGen-X, the first diffusion transformer model specifically designed for both generating and interactively controlling open-world game videos. This model facilitates high-quality, open-domain generation by simulating an extensive array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, predicting and altering future content based on the current clip, thus allowing for gameplay simulation. To realize this vision, we first collected and built an Open-World Video Game Dataset from scratch. It is the first and largest dataset for open-world game video generation and control, which comprises over a million diverse gameplay video clips sampling from over 150 games with informative captions from GPT-4o. GameGen-X undergoes a two-stage training process, consisting of foundation model pre-training and instruction tuning. Firstly, the model was pre-trained via text-to-video generation and video continuation, endowing it with the capability for long-sequence, high-quality open-domain game video generation. Further, to achieve interactive controllability, we designed InstructNet to incorporate game-related multi-modal control signal experts. This allows the model to adjust latent representations based on user inputs, unifying character interaction and scene content control for the first time in video generation. During instruction tuning, only the InstructNet is updated while the pre-trained foundation model is frozen, enabling the integration of interactive controllability without loss of diversity and quality of generated video content.

1 INTRODUCTION

GameGen-X addresses the challenge of generating and interactively controlling complex open-world game content, introducing a model that responds to user inputs. It is trained on OGameData, a large game-video dataset built for generation and control.

1 INTRODUCTION

  • Developing open-world game prototypes is resource-intensive, involving substantial work in concept design, asset creation, programming, and preliminary testing.The introduction notes that small teams could spend months building functional early-stage prototypes.Source: §1 · pp. 1–2
  • Prior neural game simulators mainly addressed 2D or early 3D games, leaving complex open-world content generation and meaningful interactive control as challenges.Open-world games involve intricate environments, dynamic events, diverse characters, and complex actions.Source: §1 · p. 2
  • GameGen-X introduces a diffusion transformer for generating diverse open-world game content and producing video clips that respond to multimodal user inputs.The generated clips evolve from an initial setup of the environment and characters, simulating a game-like experience.Source: §1 · p. 2
  • GameGen-X uses two training stages: foundation-model pre-training on OGameData for text-to-video and continuation, followed by instruction tuning of InstructNet while the foundation model is frozen.InstructNet maps structured environment instructions and keyboard controls to generated game content.Source: §1 · p. 2
  • OGameData contains one million clips from over 150 next-generation games, with separate subsets for generative-model training and interactive control.The dataset was built with a human-in-the-loop pipeline involving scoring, filtering, sorting, and structural captioning.Source: §1 · p. 3
  • GameGen-X is reported to excel at high-quality game-content generation and to offer superior control over environments and characters.The paper describes extensive comparisons with open-source and commercial models.Source: §1 · p. 3

2 OGAMEDATA: LARGE-SCALE FINE-GRAINED GAME DOMAIN DATASET

OGameData is a game-specific dataset for open-world video generation and interactive control, with separate subsets for those tasks. Its construction combines automated processing with human expertise, and its captions are tailored to generation or interactive scene changes.

2 OGAMEDATA: LARGE-SCALE FINE-GRAINED GAME DOMAIN DATASET

  • OGameData has generation and instruction subsets, respectively tailored to foundation-model training and instruction tuning for interactive control.The dataset is designed specifically for open-world game video generation and control.Source: §2 · p. 3

2.1 DATASET CONSTRUCTION PIPELINE

  • The construction pipeline combines collection, cleaning, segmentation, filtering, and structured caption annotation with both AI and human expertise.The paper states that automated techniques alone are insufficient for domain-specific game intricacies.Source: §2.1 · p. 4
  • Collection draws on internet videos, local game engines, and existing datasets across more than 150 next-generation games, then filters clips and checks metadata with human experts.Clips shorter than four seconds are discarded, and longer clips are split into 16-second segments.Source: §2.1 · p. 4
  • Generation captions describe metadata, scene context, and key characters, while instruction captions describe scene changes between initial and subsequent frames.The two captioning strategies serve text-to-video generation and interactive generation, respectively.Source: §2.1 · p. 4

2.2 DATASET SUMMARY

  • OGameData comprises one million clips curated by more than 10 human experts, with GPT-4o captions and game-specific content such as game names, player perspectives, and character details.The paper reports that it has finer-grained annotations than Miradata and includes generation and control subsets.Source: §2.2 · p. 4

3 GAMEGEN-X

GameGen-X pairs a text-conditioned foundation model for initial open-world game clips with InstructNet for user-controlled continuation. Its framework combines video generation with controls that steer future content.

3.1 GAME VIDEO GENERATION AND INTERACTION

  • A text prompt specifies the initial scene, while time-varying multimodal controls steer character movement and scene dynamics as the clip is generated.Source: §3.1 · p. 5

3.2 FOUNDATION MODEL TRAINING FOR GENERATION

  • A masked spatial-temporal diffusion transformer combines spatial, temporal, and text cross-attention, while frame masking unifies text-to-video generation and context-preserving continuation.Source: §3.2 · p. 6

3.3 INSTRUCTION TUNING FOR INTERACTIVE CONTROL

  • With the foundation model frozen, InstructNet’s operation and instruction fusion experts modify future predictions from user controls while preserving natural continuation without input.Keyboard inputs primarily influence motion direction and character control, while instruction text controls complex scene elements; video prompts are optional except for complex-action generation or editing.Source: §3.3 · pp. 6–7

4 EXPERIMENTS

The experiments report strong generation performance and superior interactive control for GameGen-X, with qualitative examples spanning varied game content and user-directed changes. Ablations indicate that the data strategy and control components contribute to the reported capabilities.

4.1 QUANTITATIVE RESULTS

  • GameGen-X performs well on FID, FVD, TVA, MS, and SC, and exhibits superior control ability to the compared models.Table 2 evaluates generation against four open-source models; Table 3 evaluates control, including response to character-action and environment-event signals.Source: §4.1 · p. 8, Table 2 · p. 8, Table 3 · p. 8
  • The data strategy outperforms alternatives on semantic consistency, distribution alignment, and user preference, while visual-quality metrics are comparable.The alternatives include MiraData, short captions, and progression training.Source: §4.1 · p. 8, Table 4 · p. 8
  • Removing InstructNet significantly reduces SR and UP, while removing Instruct Captions or decomposition also lowers control metrics to a lesser extent.The ablation evaluates these components’ effects on interactive control ability.Source: Table 5 · p. 8, §4.1 · p. 9

4.2 QUALITATIVE RESULTS

  • Qualitative examples show generation of varied characters, environments, actions, and events, alongside control of lighting, atmosphere, and character movement.Text instructions and keyboard inputs guide environmental events and character actions, respectively.Source: §4.2 · p. 9, Figure 5 · p. 9, Figure 6 · p. 9
  • In qualitative comparisons, GameGen-X better meets game-content requirements than open-source models and maintains camera logic while controlling a character exiting a cave.Among commercial models, only Pika, Kling1.5, and GameGen-X initially followed the cave-exit description; GameGen-X and Kling1.5 then successfully guided the character out.Source: §4.2 · pp. 9–10, Figure 7 · p. 10, Figure 8 · p. 10

5 CONCLUSION

GameGen-X combines open-world game-video generation with multimodal interactive control, and the paper presents it as a potential auxiliary tool for game design. The related-work discussion places this goal amid video diffusion advances and earlier game simulators focused on simpler games.

5 CONCLUSION

  • GameGen-X is presented as a diffusion transformer for generating and controlling open-world game videos, with dynamic environments, complex characters, and interactive gameplay.The conclusion describes it as an initial step toward a new game-design paradigm, while noting that practical challenges remain.Source: §5 · pp. 10–11
  • The paper suggests GameGen-X could serve as a scalable, efficient auxiliary tool to traditional game design and support data-driven early-stage content creation.The conclusion frames this as potential and says challenges for practical application remain.Source: §5 · p. 11

A.1 VIDEO DIFFUSION MODELS

  • Prior video diffusion work had advanced image- and text-conditioned generation, but gaming video generation and interactive controllability remained under-explored.The discussion also describes DiT-based video-generation developments and earlier U-Net scalability issues.Source: §A.1 · p. 16

A.2 GAME SIMULATION AND INTERACTION

  • Earlier game simulators focused on simpler games, while GameGen-X targets complex next-generation environments and generates continuations responsive to multimodal player controls.The related-work passage contrasts Atari and DOOM-era simulations with open-world environments, events, characters, and actions.Source: §A.2 · p. 16

B.1 DATA AVAILABILITY STATEMENT AND CLARIFICATION

The dataset is assembled from publicly available sources, with release and use boundaries tied to the original videos’ ownership and research-only terms. Its construction combines human review with automated segmentation, filtering, annotation, and structured captioning.

B.1 DATA AVAILABILITY STATEMENT AND CLARIFICATION

  • The release supplies YouTube video IDs rather than the original raw videos, and the dataset is described as informational only.The institution disclaims ownership of Internet-sourced videos and responsibility for their content or meaning.Source: §B.1 · pp. 16–17
  • The released data are intended exclusively for research, and the agreement does not sanction commercial use.The dataset is licensed under CC BY 4.0, while its video copyrights remain with original owners.Source: §B.1 · p. 17

B.2 CONSTRUCTION DETAILS

  • Collection prioritizes playable, high-quality footage without UI elements, drawing on online sources and local game engines across more than 150 next-generation games.The construction details report 32,000 YouTube videos and nearly 100 hours of locally recorded gameplay with synchronized keyboard signals.Source: §B.2 · p. 17
  • Human experts remove videos with UI or non-playable content and annotate game name, genre, and player perspective, yielding 15,000 metadata-complete videos.The paper notes that low-quality footage and missing metadata make automated annotation alone insufficient.Source: §B.2 · p. 17
  • Scene segmentation discards clips shorter than four seconds and splits longer clips into 16-second segments, producing around one million clips of 4–16 seconds at 24 fps.The segmentation uses TransNetV2 and PyScene to identify scene-change timestamps.Source: §B.2 · p. 17
  • Clip cleaning uses aesthetic, motion, and content-similarity scores, then annotates camera motion with CoTrackerV2.The pipeline filters clips below a manually selected aesthetic threshold, excessive or minimal motion, and overly similar content within a game.Source: §B.2 · pp. 17–18
  • Structural captions give generation clips five descriptive dimensions and instruction clips five change-oriented dimensions that can be modified independently at inference.The instruction dimensions cover environment basics, transition, lighting, action, and miscellaneous metadata; generation captions cover summary, game metadata, character, frame descriptions, and atmosphere.Source: §B.2 · p. 18
  • Separate prompt sets address free-camera footage and gameplay footage, using summary and dense prompts plus structural instructions for subsequent-frame changes.The paper associates free-camera footage with engine-specific features and gameplay footage with guiding model behavior.Source: §B.2 · p. 18

B.3 DATASET SHOWCASES

The dataset showcases pair video clips with dense structural descriptions or instructions organized around distinct content dimensions. Generation captions describe the scene and its details, while instruction captions specify changes relative to an initial frame.

B.3 DATASET SHOWCASES

  • The showcased GEN and INS captions are both structural, providing multidimensional annotations of their video clips.The paper presents four sampled cases from each dataset subset.Source: §B.3 · pp. 20–21
  • OGameData-GEN captions organize video descriptions into summary, game metadata, character information, frame descriptions, and atmosphere.The examples densely describe overall content and sequential details, including the forested lakeshore and futuristic city samples.Source: §B.3 · p. 21, Figure 10 · p. 21, Figure 9 · p. 21
  • OGameData-INS instructions use imperative descriptions to capture changes in subsequent frames across five dimensions.The dimensions are environment basics, character action, environment change, lighting and sky, and miscellaneous information; examples include forest and waterside scenes.Source: §B.3 · pp. 21–22, Figure 11 · p. 22, Figure 12 · p. 22

B.4 QUANTITATIVE ANALYSIS

The analysis characterizes game diversity in OGameData-GEN and the motion, quality, and caption properties of both dataset subsets. It also describes how OGameData-INS is curated for character-motion control.

Game-related Data Analysis

  • OGameData-INS uses five selected open-world games, exclusively third-person views, and varied character motions, with zoom-in motion common.Its motion-score histogram is mostly between 0 and 100, and aesthetic scores are mostly between 4.5 and 6.5.Source: §B.4 · p. 22, Figure 14 · p. 23
  • 52.7% of OGameData-GEN games use third-person views and 47.3% first-person views; RPG (55.23%) and ACT (33.08%) are its most common genres.Simulation accounts for 3.35% and FPS for 3.17%.Source: §B.4 · p. 22, Figure 13 · p. 23

Clips-related Data Analysis

  • OGameData-GEN motion scores are mostly 0–75, aesthetic scores mostly 4.5–6, and captions mostly 100–200 words.OGameData-INS captions are significantly shorter, a design intended to focus on instructional content.Source: Figure 14 · p. 23, §B.4 · p. 24, Figure 15 · p. 24

C.1 TRAINING STRATEGY

GameGen-X is trained in stages, with a frozen foundation model and an InstructNet branch for interactive control. The section also describes its architecture, training resources, and comparisons intended to separate dataset and framework contributions.

C.1 TRAINING STRATEGY

  • Foundation pre-training allocates 75% probability to text-to-video and 25% to video extension; instruction tuning freezes the base model and trains InstructNet for video extension.The second phase uses 720p, four-second clips and varied text, keyboard, and video controls; 20% of samples have no controls.Source: §C.1 · pp. 24–25
  • Bucket-based sampling spans resolutions from 480p to 1024×1024 and durations up to 480 frames at 24 fps, with random segments taken from longer videos.The 3D VAE compresses spatial dimensions by 8× and temporal dimensions by 4×.Source: §C.1 · p. 24, Table 6 · p. 25, §C.2 · p. 25

C.2 MODEL ARCHITECTURE

  • The architecture combines a 3D VAE, T5 text encoder, base model, and InstructNet, whose instruction, keyboard, and video-prompt pathways fuse controls into latent features.InstructNet has 28 alternating spatial and temporal blocks; the base transformer uses masking to support generation and video extension.Source: §C.2 · pp. 25, 27

C.3 COMPUTATION RESOURCES AND COSTS

  • Training used 24 NVIDIA H800 GPUs; base-model training took about 25 days and InstructNet training seven days.The infrastructure used approximately 50TB for data and checkpoints.Source: §C.3 · p. 27

D.1 FAIRNESS STATEMENT AND CONTRIBUTION DECOMPOSITION

  • The authors compare models on test content not explicitly present in training and use 10K MiraData and OGameData subsets with OpenSora to separate data and framework effects.The authors report these comparisons as fair for game-content generation because relevant baselines include game or engine-rendered scenes.Source: §D.1 · pp. 27–28
  • With OpenSora fixed, OGameData training scores (735.4, 0.76) versus MiraData (863.65, 0.71); with OGameData fixed, the framework scores (735.4, 0.76) versus OpenSora (740.5, 0.74).The pairs report averaged domain-alignment and visual-quality metrics, respectively.Source: §D.1 · p. 28, Table 7 · p. 28

D.2 EXPERIMENTAL SETTINGS

The experimental settings define generation and control evaluation datasets, a multi-metric assessment, and smaller-data ablations. The protocols cover both generated-video qualities and instruction-following control.

Evaluation Benchmark

  • OGameEval-Gen contains 50 held-out text-video pairs, while OGameEval-Ins contains 60 pairs for character and environment control evaluation.GPT-4o generated captions for fair comparison, with structural instructions for GameGen-X and dense captions for other methods.Source: §D.2 · p. 28

Metric Details

  • Evaluation combines FID, FVD, TVA, UP, SR, MS, DD, SC, and IQ to assess visual quality, alignment, control, motion, diversity, and subject consistency.FID and FVD are lower-is-better; MS, DD, SC, and IQ indicate smoother motion, richer dynamics, greater consistency, and clearer image quality at higher scores.Source: §D.2 · p. 28

Ablation Experiment Design Details

  • Ablations use 20K generation and 10K control samples, with comparisons for MiraData, short captions, progressive training, caption structure, task decomposition, and InstructNet.Generation variants train for three epochs and control variants for two; the settings also include a baseline aligned to the proposed model.Source: §D.2 · pp. 29–30

D.3 HUMAN EVALUATION DETAILS

Human evaluation uses ten gaming- and AIGC-experienced volunteers under blind presentation. Separate protocols assess visual preference, text-video alignment, and control success.

Overview

  • Ten volunteers with gaming and AIGC experience rated videos without model attribution in a blind evaluation.For user preference, they judged visual quality without seeing the accompanying prompts.Source: §D.3 · p. 30

Text-Video Alignment

  • Text-video alignment ratings assess instruction accuracy and gaming-style aesthetics using paired prompts and videos.The rubric scores complete alignment as 1, partial success as 0.5, and significant deviations as 0.Source: §D.3 · p. 30

Success Rate

  • Control success combines human judgments and PLLaVA caption analysis, averaging their scores across three generated videos per prompt.Successful control requires instruction adherence and smooth, natural transitions without visual discontinuities.Source: §D.3 · pp. 30–31

D.4 ANALYSIS OF GENERATION SPEED AND CORRESPONDING PERFORMANCE

The analysis examines generation speed and visual quality across hardware, resolutions, and sampling steps. More sampling steps improve visual quality, while higher resolutions and more steps increase generation time.

Generation Speed and Performance

  • The analysis recorded generation time, FPS, and VBench metrics in 30 open-domain inference runs on one A800 and one H800 GPU.The reported metrics include SC, background consistency, DD, aesthetic quality, IQ, and their average Overall score.Source: §D.4 · p. 31, Table 10 · p. 31
  • At 320x256 with 10 sampling steps, the model produced visually coherent, texture-rich video at 20 FPS on a single H800 GPU.The authors describe this setting as suitable for acceptable imaging quality and relatively high FPS at lower resolutions and fewer steps.Source: §D.4 · p. 31, Figure 16 · p. 32

Performance Analysis

  • Overall scores rose from 0.737 to 0.800 at 848x480 and from 0.655 to 0.812 at 1280x720 when sampling steps increased from 10 to 50.The analysis reports that higher resolutions typically require more sampling steps to reach optimal visual quality.Source: Table 11 · p. 31, §D.4 · p. 31
  • At 848x480, increasing sampling steps from 5 to 50 improves visual quality, with 30 and 50 steps enriching textures, diversity, and coherence.Figure 17 illustrates outputs at 5, 10, 30, and 50 steps.Source: §D.4 · p. 31, Figure 17 · p. 32

D.5 FURTHER QUALITATIVE EXPERIMENTS

The qualitative experiments show varied generated characters, actions, environments, and events, alongside comparisons of open-domain generation and interactive control. They also demonstrate video-prompt conditioning with motion vectors and Canny sequences.

Basic Functionality

  • GameGen-X generates diverse characters and scenes, varied actions such as flying and biking, seasonal environments, and weather events including fog, snowfall, rain, and ocean waves.Figures illustrate action, environmental, and event examples.Source: §D.5 · p. 33, Figure 19 · p. 34, Figure 20 · p. 34

Open-domain Generation Comparison

  • Across structural, short, and dense prompts, the authors report more coherent instruction-following than CogVideoX and OpenSora-Plan in selected examples.Examples include stormy-wasteland lighting and motion, glacier details under short captions, and golden armor and cliff details under a dense caption.Source: §D.5 · p. 33, Figure 24 · p. 36, Figure 29 · p. 39

Interactive Control Ability Comparison

  • In commercial-model comparisons, GameGen-X follows text instructions for character direction and environmental changes that several other models struggle to control.The reported examples include turning, clearing fog, exiting a cave, and darkening a sky; Kling1.5 also succeeds on some control tasks.Source: §D.5 · p. 40, Figure 31 · p. 40, Figure 32 · p. 41

Video Prompt

  • Video prompts, including motion vectors and Canny sequences, provide additional control inputs for customized scene generation.The same motion pattern is used across different text-described environments, and the same Canny sequence supports scenes tailored to different text inputs.Source: §D.5 · p. 40, Figure 36 · p. 44, Figure 37 · p. 45

E.1 LIMITATIONS

The paper identifies constraints in real-time generation, long-term consistency, complex actions, visual realism, and practical integration. It also outlines future directions spanning optimization, data and architecture, and applications beyond gaming.

E.1 LIMITATIONS

  • Diffusion sampling and spatial-temporal attention create computational challenges for real-time interaction, while memory and processing constraints prevent ultra-high-resolution 2K/4K generation.The authors state that this restricts applicability in high-end game development.Source: §E.1 · p. 46
  • Autoregressive errors can accumulate over long sequences, and the 1–108-frame memory window contributes to scene changes when revisiting environments.The authors identify improving memory as necessary for better long-term scene retention.Source: §E.1 · p. 46
  • Fast complex actions can exceed the model’s motion capacity, while generative outputs do not consistently obey physical laws such as realistic reflections and character-environment interactions.The paper also reports dataset-related limits on managing multiple characters, especially coordinated combat or cooperative tasks.Source: §E.1 · p. 46
  • Video outputs are not directly compatible with existing game engines; converting them into 3D models is offered as a possible bridge.The paper frames this as a present integration gap, not an established conversion solution.Source: §E.1 · p. 46

E.2 POTENTIAL FUTURE WORKS

  • For real-time use, future work proposes lighter diffusion models and hybrid or distillation approaches to reduce inference cost while balancing speed and quality.These directions address the computational cost that hinders real-time generation.Source: §E.2 · pp. 46–47
  • Map-based constraints and character-conditioned embeddings are proposed for long-term consistency, while broader game data could improve generalization across genres and mechanics.The dataset expansion proposal includes user-generated content, modding tools, and procedurally generated worlds.Source: §E.2 · p. 47
  • Motion-aware components, higher-frame-rate data, and external signals such as motion vectors or pose prompts are proposed to improve fast-action generation and reduce video-prompt dependence.The proposed goal includes more autonomous generation of complex, fast-moving actions.Source: §E.2 · p. 47
  • Future directions include full 3D attention, unified video generation and understanding, 3D Gaussian Splatting, and extending simulation techniques to real-world applications.The paper describes 3D Gaussian Splatting as a route toward realistic object dynamics and proposes applications such as autonomous-vehicle testing and virtual training.Source: §E.2 · pp. 47–48
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