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KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation, is the first 3D generative model trained on agent-authored part data and finds that whole-object fidelity improves over the same backbone fine-tuned on the same dataset.
This work shows that complex scenes with hundreds of objects can instead be generated compositionally by adapting a strong single-object 3D generative prior to multi-view observations, and introduces UE-MeshyScene, a photorealistic benchmark of densely cluttered scenes with hundreds of objects, per-object annotations,...
AlayaRenderer-Flash is introduced, a real-time-oriented generative forward world renderer that pushes AlayaRenderer from 0.56 FPS to 31.54 FPS, reaching the speed of play.
Building interactive worlds that respond coherently to player actions has long been a shared goal of computer graphics, games, and artificial intelligence. Recent video generative models provide a data-driven route toward this goal by predicting future observations conditioned on user actions, and are increasingly rega...
This work proposes Reinforcement Learning with Human-Engine Verification (RLHEV), a post-training paradigm that combines dense engine signals with implicit human acceptance feedback from the development process to support RL post-training.
OrthoPilot is a clinical artificial intelligence system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care and autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patie...
AlayaWorld enables open-ended real-time interaction, allowing users to freely navigate and perform diverse actions such as combat, spell casting, and monster summoning, and the framework unifies the complete development-from data preparation model architecture, model training, inference acceleration, and deployment-wit...
WorldRover turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.
This work introduces Surprise Forcing, a training-free framework that treats both limitations as online resource-allocation problems and improves long-horizon consistency and visual quality while retaining real-time streaming throughput.
Sekai2 is introduced, a multi-source real-world video dataset that carries the world-exploration footage of Sekai toward interactive world modeling, and Corpus-scale analyses demonstrate complete pose-and-caption coverage, broad geographic and semantic diversity, varied camera trajectories, and highly non-redundant tem...
GROVE is introduced, a training-free framework that supports both behaviors with one memory grown causally from a continuous video stream, and achieves the best results among the compared methods.
This work explicitly model the evolving world state, delegate exact geometric computation to a fixed, zero-parameter renderer, and leave the neural model to synthesize appearance, establishing two properties of Marionette, a world model for interactive games with articulated characters that is directly controllable.
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