Dynamics of human body skeletons convey significant information for action recognition. Conventional approaches modeling usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties generalization. In this work, we propose a novel model dynamic called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations previous methods by automatically learning both spatial temporal patterns from data. This...

Dynamics of human body skeletons convey significant information for action recognition. Conventional approaches modeling usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties generalization. In this work, we propose a novel model dynamic called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations previous methods by automatically learning both spatial temporal patterns from data. This...

In this work, we aim to generate long actions represented as sequences of skeletons. The generated must demonstrate continuous, meaningful human actions, while maintaining coherence among body parts. Instead generating skeletons sequentially following an autoregressive model, propose a framework that generates the entire sequence altogether by transforming from latent vectors sampled Gaussian process (GP). This framework, named Convolutional Sequence Generation Network (CSGN), jointly models...

The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications. Previous approaches for scene-aware synthesis are constrained by pre-defined target objects or positions and thus limit the diversity of human-scene interactions synthesized motions. In this paper, we focus on problem synthesizing diverse motions under guidance action sequences. To achieve this, first decompose into three aspects, namely interaction (e.g. sitting different...

Visually-guided robot grinding is a novel and promising automation technique for blade manufacturing. One common problem encountered in hand-eye calibration, which establishes the pose relationship between end effector (hand) scanning sensor (eye). This paper proposes new calibration approach belt grinding. The main contribution of this its consideration both joint parameter errors equation. objective function built solved, from 30 compensated values (corresponding to 24 parameters six...

Robotic grinding is a promising technique to generate the final shape of blades. It can relieve human from participating in dirty and noisy environments, improve product quality, lower production costs. One important task robotic 3-D matching. However, existing matching methods do not consider requirements associated with different allowances, which potentially lead an unstable force. This paper proposes novel method for grinding. The goal define new objective function considering allowance...

Fashion landmarks are functional key points defined on clothes, such as corners of neckline, hemline, and cuff. They have been recently introduced [18]as an effective visual representation for fashion image understanding. However, detecting challenging due to background clutters, human poses, scales. To remove the above variations, previous works usually assumed bounding boxes clothes provided in training test additional annotations, which expensive obtain inapplicable practice. This work...

We present a unified and flexible framework to address the generalized problem of 3D motion synthesis that covers tasks prediction, completion, interpolation, spatial-temporal recovery. Since these have different input constraints various fidelity diversity requirements, most existing approaches only cater specific task or use architectures tasks. Here we propose based on Conditional Variational Auto-Encoder (CVAE), where treat any arbitrary as masked series. Notably, by considering this...

We revisit human motion synthesis, a task useful in various real-world applications, this paper. Whereas number of methods have been developed previously for task, they are often limited two aspects: 1) focus on the poses while leaving location movement behind, and 2) ignore impact environment motion. In paper, we propose new framework, with interaction between scene taken into account. Considering uncertainty motion, formulate as generative whose objective is to generate plausible...

Flexible and safe human-robot collaboration depends on accurately capturing the three-dimensional motion of humans robots in field smart manufacturing. In this paper, a novel approach to developing collaborative assembly system is proposed applied digital twins. Within context, deep learning-based model explored develop depth camera-based human recognition for accurate prediction key points skeletons high-precision localisation setting. After functional mapping robot calibration, collision...

Reducing inconsistencies in the behavior of different versions an AI system can be as important practice reducing its overall error. In image classification, sample-wise appear "negative flips": A new model incorrectly predicts output for a test sample that was correctly classified by old (reference) model. Positive-congruent (PC) training aims at error rate while same time negative flips, thus maximizing congruency with reference only on positive predictions, unlike distillation. We propose...

AbstractBlades play an important role in aviation engine, gas turbine and jet engine. Inspecting the blade by optical method is a meaningful work manufacturing industry. During inspecting process, one common problem encountered that scanned point cloud large scale noisy. In this paper, we present systematic introduction of simplification, smoothing parameter extraction with respect to point-sampled blades. First, moving least square surface applied create geometric deviation, which used...

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic path planning for robotic cavity machining other related fields. Traditional methods typically incorporate subregion division procedure that leads to excessive bridging, requiring longer, more sharply turning, unevenly spaced spirals achieve coverage. To address this issue, paper proposes novel method using conformal slit mapping. It takes advantage the fact mapping...

We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing simple yet effective representation keypoint motion, pairwise encoding, is introduced. design graph convolutional network architecture, U-shaped GCN (UGCN). It captures both short-term and long-term information to fully leverage additional supervision loss. experiment training UGCN with on two large scale benchmarks: Human3.6M MPI-INF-3DHP. Our model surpasses...

Fashion landmarks are functional key points defined on clothes, such as corners of neckline, hemline, and cuff. They have been recently introduced an effective visual representation for fashion image understanding. However, detecting challenging due to background clutters, human poses, scales. To remove the above variations, previous works usually assumed bounding boxes clothes provided in training test additional annotations, which expensive obtain inapplicable practice. This work addresses...

Abstract The femtosecond laser-induced grating scattering (fs-LIGS) technique has recently been developed and applied for temperature pressure measurements. In this work, we combined deep learning with the fs-LIGS to predict gas-phase from raw signals without data post-processing. Two models, a fully connected neural network convolutional network, were trained master hidden relationship between features of signal traces corresponding under which was recorded. Accurate predictions by both...

We present a generative dialogue system capable of operating in full-duplex manner, allowing for seamless interaction. It is based on large language model (LLM) carefully aligned to be aware perception module, motor function and the concept simple finite state machine (called neural FSM) with two states. The modules operate simultaneously, simultaneously speak listen user. LLM generates textual tokens inquiry responses makes autonomous decisions start responding to, wait for, or interrupt...

In this work, we explore the possibility of training high-parameter 3D Gaussian splatting (3DGS) models on large-scale, high-resolution datasets. We design a general model parallel method for 3DGS, named RetinaGS, which uses proper rendering equation and can be applied to any scene arbitrary distribution primitives. It enables us scaling behavior 3DGS in terms primitive numbers resolutions that were difficult before surpass previous state-of-the-art reconstruction quality. observe clear...