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Added by
Xiaoli Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Predicting future human motion is critical for intelligent robots to interact with humans in the real world, and human motion has the nature of multi-granularity. However, most of the existing work either implicitly modeled multi-granularity information via fixed modes or focused on modeling a single granularity, making it hard to well capture this nature for accurate predictions. In contrast, we propose a novel end-to-end network, Semi-Decoupled Multi-grained Trajectory Learning network (SDMTL), to predict future poses, which not only flexibly captures rich multi-grained trajectory information but also aggregates multi-granularity information for predictions. Specifically, we first introduce a Brain-inspired Semi-decoupled Motion-sensitive Encoding module (BSME), effectively capturing spatiotemporal features in a semi-decoupled manner. Then, we capture the temporal dynamics of motion trajectory at multi-granularity, including fine granularity and coarse granularity. We learn multi-grained trajectory information using BSMEs hierarchically and further capture the information of temporal evolutional directions at each granularity by gathering the outputs of BSMEs at each granularity and applying temporal convolutions along the motion trajectory. Next, the captured motion dynamics can be further enhanced by aggregating the information of multi-granularity with a weighted summation scheme. Finally, experimental results on two benchmarks, including Human3.6M and CMU-Mocap, show that our method achieves state-of-the-art performance, demonstrating the effectiveness of our proposed method. The code will be available if the paper is accepted.
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Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN.
Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. In this context, we study the use of different types of attention, computed at joint, body part, and full pose levels. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW validate the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at https://github.com/wei-mao-2019/HisRepItself.
Human motion prediction is a challenging and important task in many computer vision application domains. Existing work only implicitly models the spatial structure of the human skeleton. In this paper, we propose a novel approach that decomposes the prediction into individual joints by means of a structured prediction layer that explicitly models the joint dependencies. This is implemented via a hierarchy of small-sized neural networks connected analogously to the kinematic chains in the human body as well as a joint-wise decomposition in the loss function. The proposed layer is agnostic to the underlying network and can be used with existing architectures for motion modelling. Prior work typically leverages the H3.6M dataset. We show that some state-of-the-art techniques do not perform well when trained and tested on AMASS, a recently released dataset 14 times the size of H3.6M. Our experiments indicate that the proposed layer increases the performance of motion forecasting irrespective of the base network, joint-angle representation, and prediction horizon. We furthermore show that the layer also improves motion predictions qualitatively. We make code and models publicly available at https://ait.ethz.ch/projects/2019/spl.
In this paper, we propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion. Previous works commonly rely on RNN-based models considering shorter forecast horizons reaching a stationary and often implausible state quickly. Instead, our focus lies on the generation of plausible future developments over longer time horizons. To mitigate the issue of convergence to a static pose, we propose a novel architecture that leverages the recently proposed self-attention concept. The task of 3D motion prediction is inherently spatio-temporal and thus the proposed model learns high dimensional embeddings for skeletal joints followed by a decoupled temporal and spatial self-attention mechanism. This allows the model to access past information directly and to capture spatio-temporal dependencies explicitly. We show empirically that this reduces error accumulation over time and allows for the generation of perceptually plausible motion sequences over long time horizons up to 20 seconds as well as accurate short-term predictions. Accompanying video available at https://youtu.be/yF0cdt2yCNE.
Predicting future human motion plays a significant role in human-machine interactions for a variety of real-life applications. In this paper, we build a deep state-space model, DeepSSM, to predict future human motion. Specifically, we formulate the human motion system as the state-space model of a dynamic system and model the motion system by the state-space theory, offering a unified formulation for diverse human motion systems. Moreover, a novel deep network is designed to build this system, enabling us to utilize both the advantages of deep network and state-space model. The deep network jointly models the process of both the state-state transition and the state-observation transition of the human motion system, and multiple future poses can be generated via the state-observation transition of the model recursively. To improve the modeling ability of the system, a unique loss function, ATPL (Attention Temporal Prediction Loss), is introduced to optimize the model, encouraging the system to achieve more accurate predictions by paying increasing attention to the early time-steps. The experiments on two benchmark datasets (i.e., Human3.6M and 3DPW) confirm that our method achieves state-of-the-art performance with improved effectiveness. The code will be available if the paper is accepted.
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