Newest papers

AnyGameRenderer: Entity-Consistent Generative Rendering Across Games and an Entity-Level Benchmark

Ruicong Liu, Liangyang Ouyang, Fengbo Lan, Zhixiang Wang, Kaipeng Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31466 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

从GE对应的编码代理人 riscreated数据编码器现在可以在几分钟内构建小数据可播放的游戏,但结果通常看起来像编程艺术:灰色封锁,通过彩色地理原理,和平面的语义时间blobs。我们介绍了AnyGameRenderer,一个视频到视频的生成器,它将这些映像转化为高质量的图像,基于字符,动作细节和效果。在整个过程中,它保持布局,摄像头和每个实体的位置和时间不变。我们的AnyGameRenderer概括了代理人构建的各种类型,如塔防,自动战士,顶部下方的幸存者,基于旋转的战斗,卡车赛事和人群跑者。AnyGameRenderer将平面视频

Machine translation (Meta M2M100); the English original follows.

Abstract

AI coding agents can now build small playable games in minutes, but the results usually look like programmer art: gray blockouts, colored geometric primitives, and flat semantic blobs. We present AnyGameRenderer, a video-to-video generative renderer that turns such footage into high-quality visuals with characters, motion detail, and effects. Throughout, it keeps the layout, camera, and every entity's position and timing intact. Our AnyGameRenderer generalizes across a wide range of genres that agents build, such as tower defense, auto-battlers, top-down survivors, turn-based battles, kart racing, and crowd runners. AnyGameRenderer takes plain video as its game-state input, with no complex representations such as engine G-buffers, per-object class labels, or low-poly proxies. Gray blockouts, colored primitives, semantic maps, and wireframes all work as input. It also preserves entity correspondence: group counts stay correct, and entities appear and disappear on the right frame. We implement AnyGameRenderer by LoRA fine-tuning MiniMax-H3 ref2va, a 33B reference-video-conditioned video generation model. Training uses $\sim$10k paired clips from three sources: (i) multi-stream rendering data from 38 agent-made games exported from GEAR, an AI game-creation tool; (ii) two-stream rendering data from Unreal Engine, with programmatically controlled crowds, count changes, and entity scales; and (iii) real gameplay recordings, with pairs reconstructed via 3D-box detection. To evaluate the entity correspondence, we propose an entity-level benchmark of 59 held-out cases with 753 entities, scored against ground-truth boxes. Compared with the base model, AnyGameRenderer better preserves entity correspondence: temporal match rises from 50.9\% to 62.9\%, missed count changes fall from 34.7\% to 21.3\%, and fewer entities are hallucinated or left unrendered.

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Topics: Computer science, Rendering (computer graphics), Artificial intelligence, Hallucinating, CLIPS, Generative grammar

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange