Vision Pretraining for Dense Spatial Perception
Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structu...
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Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structu...
Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task ex...
3D reconstruction and generation are commonly tackled by separate paradigms: pixel-based regression for reconstructio...
Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling d...
Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answe...
We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interacti...
Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabi...
We present EVA-Client, an open-source framework for deployment, data collection, and evaluation of trained manipulati...
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from r...
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and th...
Increasingly, LLM inference services proxy client requests to engine replicas distributed globally. Load-balancing po...
Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel...