We present COMO, a real-time monocular mapping and odometry system that
encodes dense geometry via a compact set of 3D anchor points. Decoding anchor
point projections into dense geometry via per-keyframe depth covariance
functions guarantees that depth maps are joined together at visible anchor
points. The representation enables joint optimization of camera poses and dense
geometry, intrinsic 3D consistency, and efficient second-order inference. To
maintain a compact yet expressive map, we introduce a frontend that leverages
the covariance function for tracking and initializing potentially visually
indistinct 3D points across frames. Altogether, we introduce a real-time system
capable of estimating accurate poses and consistent geometry.

COMO 系统是一个实时单目建图和里程计系统，通过一组三维锚点来编码密集几何信息，通过锚点投影解码密集几何，保证深度图在可见锚点处完整连接，该表示允许相机位姿和密集几何的联合优化，实现内在的三维一致性和高效的二阶推断，并引入一个前端来跟踪和初始化可能在帧间视觉上无法区分的三维点，整体上，提出了一个能够估计准确位姿和一致几何的实时系统。

COMO：紧凑映射与里程计

COMO: Compact Mapping and Odometry

Text-to-motion models excel at efficient human motion generation, but
existing approaches lack fine-grained controllability over the generation
process. Consequently, modifying subtle postures within a motion or inserting
new actions at specific moments remains a challenge, limiting the applicability
of these methods in diverse scenarios. In light of these challenges, we
introduce CoMo, a Controllable Motion generation model, adept at accurately
generating and editing motions by leveraging the knowledge priors of large
language models (LLMs). Specifically, CoMo decomposes motions into discrete and
semantically meaningful pose codes, with each code encapsulating the semantics
of a body part, representing elementary information such as "left knee slightly
bent". Given textual inputs, CoMo autoregressively generates sequences of pose
codes, which are then decoded into 3D motions. Leveraging pose codes as
interpretable representations, an LLM can directly intervene in motion editing
by adjusting the pose codes according to editing instructions. Experiments
demonstrate that CoMo achieves competitive performance in motion generation
compared to state-of-the-art models while, in human studies, CoMo substantially
surpasses previous work in motion editing abilities.

CoMo 是一种可控运动生成模型，通过利用大型语言模型的先验知识，在准确生成和编辑动作方面表现出色。它将动作分解为离散且语义上有意义的姿势代码，每个代码都包含身体部位的语义信息，如 “左膝稍微弯曲”。通过调整姿势代码，CoMo 可以实现直接干预动作编辑。实验证明，与最先进的模型相比，CoMo 在运动生成方面具有竞争力，在人体研究中，CoMo 在动作编辑能力方面远远超过以前的工作。