Recent advances in skeleton-based action recognition have been primarily driven by Graph Convolutional Networks (GCNs) and skeleton transformers. While conventional approaches focus on modeling joint co-occurrences through skeletal connections, they overlook the inherent positional information in 3D coordinates. Although Hyper-Graphs partially address the limitation of pairwise aggregation in capturing higher-order kinematic dependencies, challenges remain in their topological definitions. To solve these problems, this paper proposes a skeleton-to-point network (Skeleton2Point) to model joints’ position relationships directly in three-dimensional space without fixed topology limitation, which is the first to regard skeleton recognition as point clouds. However, simply considering the raw 3D coordinates would result in the loss of the anatomical identity of each keypoint and its temporal position in the sequence. To address this limitation, we augment the three-dimensional spatial coordinates with two additional dimensions: the anatomical index of each keypoint and its corresponding frame number with a proposed Information Transform Module (ITM). This transformation extends the representation from a three-dimensional to a five-dimensional feature space. Furthermore, we propose a Cluster-Dispatch-Based Interaction module (CDI) to enhance the discrimination of local-global information. In comparison with existing methods on NTU-RGB+D 60 and NTU-RGB+D 120 datasets, Skeleton2Point has demonstrated state-of-the-art performance on both joint modality and stream fusion. Especially, on the challenging NTU-RGB+D 120 dataset under the X-Sub and X-Set setting, the accuracies reach 90.63% and 91.92%.

While vision-language models (VLMs) have demonstrated promising capabilities in reasoning and planning for embodied agents, their ability to comprehend physical phenomena, particularly within structured 3D environments, remains severely limited. To close this gap, we introduce PhyBlock, a progressive benchmark designed to assess VLMs on physical understanding and planning through robotic 3D block assembly tasks. PhyBlock integrates a novel four-level cognitive hierarchy assembly task alongside targeted Visual Question Answering (VQA) samples, collectively aimed at evaluating progressive spatial reasoning and fundamental physical comprehension, including object properties, spatial relationships, and holistic scene understanding. PhyBlock includes 2600 block tasks (400 assembly tasks, 2200 VQA tasks) and evaluates models across three key dimensions: partial completion, failure diagnosis, and planning robustness. We benchmark 23 state-of-the-art VLMs, highlighting their strengths and limitations in physically grounded, multi-step planning. Our empirical findings indicate that the performance of VLMs exhibits pronounced limitations in high-level planning and reasoning capabilities, leading to a notable decline in performance for the growing complexity of the tasks.Error analysis reveals persistent difficulties in spatial orientation and dependency reasoning.We position PhyBlock as a unified testbed to advance embodied reasoning, bridging vision-language understanding and real-world physical problem-solving.