REVIEW 21 cited by
GameGen-X: Interactive Open-world Game Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 21 Pith papers
-
ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling
Distributed video-generation agents can create a spatiotemporally consistent shared world by tiling four views, exchanging cross-agent attention, and querying a spatial memory cache.
-
UCM: Unified Modeling of Camera Control and Memory with Time-aware Positional Encoding Warping for World Models
A video-generation world model that warps positional encodings of memory frames to target viewpoints achieves state-of-the-art long-term consistency and camera control.
-
WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
WorldPlay uses dual action representation, reconstituted context memory, and context forcing distillation to produce consistent 720p streaming video at 24 FPS for interactive world modeling.
-
Precise Action-to-Video Generation Through Visual Action Prompts
Skeleton-based visual action prompts give precise, cross-domain action control for video generation of human and robot interactions.
-
Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation
Layout-Togglable storytelling is introduced: diffusion transformers conditioned on layout enable precise control over character position and appearance, supported by a new large-scale dataset and benchmark.
-
Unified Vision-Language-Action Model
A single autoregressive transformer trained with discrete image, language, and action tokens, then post-trained as a video world model, reaches state-of-the-art success rates on CALVIN, LIBERO, and SimplerEnv-Bridge.
-
From Virtual Games to Real-World Play
A chunk-wise video diffusion model trained on labeled game data plus unlabeled real footage transfers game-style control commands to real-world entities.
-
Matrix-Game: Interactive World Foundation Model
A 17B-parameter diffusion model generates controllable, physically consistent Minecraft video from a reference image and user actions, beating Oasis and MineWorld on a new benchmark.
-
Video World Models with Long-term Spatial Memory
An autoregressive video world model with a persistent static point-cloud spatial memory and sparse episodic keyframes improves revisit consistency over point-cloud-conditioned baselines.
-
VideoDPO: Omni-Preference Alignment for Video Diffusion Generation
VideoDPO shows that DPO-style training on automatically selected best and worst video pairs improves overall VBench scores on three open text-to-video models, with some sub-metrics degrading and weak gains on external...
-
The Matrix: Infinite-Horizon World Generation with Real-Time Moving Control
A 2.7B parameter diffusion model trained on game and internet footage generates control-responsive 720p video streams, but the paper's 'infinite, real-time, zero-shot' claims are not backed by public benchmarks or rel...
-
Distilling Physical Priors into Streaming World Models
PhyS adds physics-aware video data, teacher distillation, and windowed reward routing to make streaming world models generate more physically plausible long rollouts.
-
Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention
Augmenting a diffusion video transformer with an RNN memory block and frame-wise overlapping attention improves long-horizon consistency, with simple LSTM matching newer Mamba2 and TTT memory blocks.
-
EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
EvoCurr couples an LLM curriculum designer with an LLM code-generating solver, but its only reported success is 1 of 5 runs and no direct baseline is shown.
-
Numerical Study of Oblique Detonation Initiation Assisted by Local Energy Deposition
Pulsatile local energy deposition can initiate sustainable oblique detonation on a finite wedge with less than 10% of the average power needed by continuous deposition.
-
Impact-driven Context Filtering For Cross-file Code Completion
The manuscript's abstract claims a new code-completion filtering method, yet the body contains an unrelated 3D animation paper, leaving the claimed work unverifiable.
-
RoboScape: Physics-informed Embodied World Model
RoboScape jointly learns RGB video, depth, and keypoint-token consistency in one autoregressive world model, improving video quality, geometry, action control, synthetic-data policy training, and policy evaluation for...
-
FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers
FullDiT2 accelerates FullDiT-style in-context conditioning for video by dynamic token selection and selective context caching, cutting per-step time by 2-3x with minimal quality loss.
-
VRAG: Learning World Models for Interactive Video Generation
VRAG improves long-horizon interactive video generation by conditioning autoregressive diffusion on retrieved historical frames and explicit global state, outperforming long-context baselines on the tested Minecraft a...
-
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving
DriveX predicts future latent BEV features from driving video and shows consistent, modest gains on occupancy, flow, and end-to-end driving, though no code is released.
-
A Survey of Interactive Generative Video
A survey that divides interactive generative video research into five modules: generation, control, memory, dynamics, and intelligence.
Discussion (0). Continue with ORCID to comment.